Aircraft state estimation method, apparatus, storage medium, and electronic device

Abstract

The application provides an aircraft state estimation method and device, a storage medium and an electronic device, and relates to the technical field of target tracking. The method comprises the following steps: determining a historical global interactive multiple model state and determining a historical current source measurement noise sufficient statistic state; determining a current source historical measurement noise based on current track point data and the historical current source measurement noise sufficient statistic state, so as to determine a current source applicable measurement noise; determining a current global interactive multiple model state of a target aircraft at a current event time based on the current source applicable measurement noise and the historical global interactive multiple model state; determining a current global state of the target aircraft at the current event time based on the current global interactive multiple model state; and determining a source measurement noise sufficient statistic state of the target aircraft at the current event time based on the current source applicable measurement noise. The embodiment of the application can improve the accuracy of aircraft state estimation.

Description

Technical Field

[0001] This invention relates to the field of target tracking technology, and in particular to an aircraft state estimation method, apparatus, storage medium, and electronic device. Background Technology

[0002] Currently, aircraft in low-altitude airspace are characterized by a variety of target types, low flight altitude, rapid changes in motion status, significant terrain and building obstruction, unstable communication links, and limited coverage of a single monitoring node. To continuously display target position, speed, motion trends, and predict flight paths, it is typically necessary to simultaneously receive multiple types of external trackpoint data, including but not limited to data reported by the aircraft's direct connection system, data received and forwarded by broadcast base stations, and trackpoint data processed by radar, electro-optical, or radio monitoring platforms. Multiple trackpoint sources (i.e., data sources) can arrive asynchronously and alternately at different rhythms, and each processing cycle usually involves only one trackpoint data point. To address this, related technologies typically use Kalman state estimation techniques with fixed noise parameters for state estimation. For example, process noise covariance and measurement noise covariance are usually pre-set for state estimation, which fails to accurately reflect error changes, resulting in low accuracy in aircraft state estimation. Therefore, there is currently no satisfactory solution to improve the accuracy of aircraft state estimation. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide an aircraft state estimation method, apparatus, storage medium, and electronic device to solve the problem of low accuracy in aircraft state estimation caused by related technologies. That is, embodiments of the present invention can fully statistically analyze the current state using current track point data and historical current source measurement noise to determine the current source historical measurement noise, thereby determining the current source applicable measurement noise with higher accuracy. Furthermore, the current source applicable measurement noise and historical global interactive multi-model states can be used to determine the current global state with higher accuracy, thereby effectively improving the accuracy of aircraft state estimation.

[0004] According to one aspect of the present invention, an aircraft state estimation method is provided, the method comprising: Acquire the current waypoint data of the target aircraft. The current waypoint data includes the current event time, the current field pattern, and the current source identifier, which is used to indicate the current data source. When it is detected that the current waypoint data satisfies the global state candidate update condition of the target aircraft, the historical global interactive multi-model state of the target aircraft under the previous valid event time is determined, and the historical current source measurement noise fully statistical state of the target aircraft under the previous valid event time, the current data source, and the current field mode is determined; wherein, an event time in the waypoint data used to update the global state of the target aircraft is regarded as a valid event time of the target aircraft; Based on the current trackpoint data and the sufficient statistical status of the historical current source measurement noise, the historical measurement noise of the current source is determined; and based on the historical measurement noise of the current source, the applicable measurement noise of the current source is determined. Based on the current applicable measurement noise from the current source and the historical global interactive multi-model state, the current global interactive multi-model state of the target aircraft at the current event time is determined; and based on the current global interactive multi-model state, the current global state of the target aircraft at the current event time is determined; the current global state supports updating the global estimated trajectory of the target aircraft. Based on the current applicable source measurement noise, a sufficiently statistical state of the source measurement noise of the target aircraft at the current event time is determined for use in the state estimation of the target aircraft at the next valid event time.

[0005] According to another aspect of the present invention, an aircraft state estimation apparatus is provided, the apparatus comprising: The acquisition unit is used to acquire the current waypoint data of the target aircraft. The current waypoint data includes the current event time, the current field mode, and the current source identifier, which is used to indicate the current data source. The processing unit is configured to, when detecting that the current waypoint data satisfies the global state candidate update condition of the target aircraft, determine the historical global interactive multi-model state of the target aircraft at the previous valid event time, and determine the historical current source measurement noise sufficient statistical state of the target aircraft at the previous valid event time, the current data source, and the current field mode; wherein, an event time in the waypoint data used to update the global state of the target aircraft is regarded as a valid event time of the target aircraft; The processing unit is further configured to determine the historical measurement noise of the current source based on the current track point data and the sufficient statistical state of the historical current source measurement noise; and to determine the applicable measurement noise of the current source based on the historical measurement noise of the current source. The processing unit is further configured to determine the current global interactive multi-model state of the target aircraft at the current event time based on the current source applicable measurement noise and the historical global interactive multi-model state; and to determine the current global state of the target aircraft at the current event time based on the current global interactive multi-model state; the current global state supports updating the global estimated trajectory of the target aircraft; The processing unit is further configured to determine, based on the current applicable source measurement noise, the source measurement noise of the target aircraft at the current event time, as a sufficient statistical state, for use in the state estimation of the target aircraft at the next valid event time.

[0006] According to another aspect of the present invention, an electronic device is provided, the electronic device including a processor and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to perform the methods mentioned above.

[0007] According to another aspect of the present invention, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform the methods mentioned above.

[0008] This invention provides embodiments that can acquire current waypoint data of a target aircraft. The current waypoint data includes the current event time, current field mode, and current source identifier, with the current source identifier indicating the current data source. When the current waypoint data is detected to meet the global state candidate update conditions of the target aircraft, the historical global interactive multi-model state of the target aircraft under the previous valid event time is determined, as well as the historical current source measurement noise fully statistical state of the target aircraft under the previous valid event time, current data source, and current field mode is determined. An event time in the waypoint data used to update the global state of the target aircraft is considered a valid event time for the target aircraft. Then, based on the current waypoint data and the historical current source measurement noise fully statistical state, the historical measurement noise of the current source can be determined; and based on the historical measurement noise of the current source, the applicable measurement noise for the current source can be determined. Furthermore, based on the applicable measurement noise from the current source and the historical global interactive multi-model state, the current global interactive multi-model state of the target aircraft at the current event time can be determined; and based on the current global interactive multi-model state, the current global state of the target aircraft at the current event time can be determined; the current global state supports the updating of the global estimated track of the target aircraft. Correspondingly, based on the applicable measurement noise from the current source, a sufficiently statistical state of the source measurement noise of the target aircraft at the current event time can be determined for use in the state estimation of the target aircraft at the next valid event time. It is evident that the embodiments of the present invention can determine the historical measurement noise from the current source through current track point data and the sufficiently statistical state of the current source measurement noise, thereby determining the applicable measurement noise from the current source with higher accuracy. Furthermore, the applicable measurement noise from the current source and the historical global interactive multi-model state can be used to determine the current global state with higher accuracy, effectively improving the accuracy of aircraft state estimation. Attached Figure Description

[0009] Further details, features, and advantages of the invention are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart illustrating an aircraft state estimation method according to an exemplary embodiment of the present invention is shown; Figure 2 A flowchart illustrating another aircraft state estimation method according to an exemplary embodiment of the present invention is shown; Figure 3 A schematic block diagram of an aircraft state estimation apparatus according to an exemplary embodiment of the present invention is shown; Figure 4 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present invention is shown. Detailed Implementation

[0010] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0011] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0012] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0013] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0014] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0015] It should be noted that the execution subject of the aircraft state estimation method provided in this embodiment of the invention can be one or more electronic devices, and this invention does not limit this; wherein, the electronic device can be a terminal (i.e., a client) or a server. Therefore, when the execution subject includes multiple electronic devices, and among these multiple electronic devices are at least one terminal and at least one server, the aircraft state estimation method provided in this embodiment of the invention can be jointly executed by the terminal and the server. Accordingly, the terminal mentioned herein can include, but is not limited to: smartphones, tablets, laptops, desktop computers, smartwatches, smart voice interaction devices, smart home appliances, vehicle terminals, flight equipment (such as drones), etc. The server mentioned herein can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, etc.

[0016] Optionally, embodiments of the present invention may also provide an aircraft state estimation system (also known as a low-altitude aircraft situation monitoring system, etc.). In this case, the executing entity of the aircraft state estimation method proposed in the embodiments of the present invention may also be the aircraft state estimation system, that is, the electronic device used to execute the aircraft state estimation method may be one or more electronic devices equipped with the aircraft state estimation system, etc.; the embodiments of the present invention do not limit this. For example, the aircraft state estimation system may adopt a stateful processing structure partitioned by target identifier, and may be deployed as a synchronous algorithm service, a stateful stream processing service, an edge computing node, a central computing service, or a cloud-edge collaborative system, etc., depending on the operating environment.

[0017] It should be noted that the application scenarios of the aircraft state estimation method in this embodiment of the invention are not limited. For example, the aircraft state estimation method proposed in this embodiment of the invention can be applied to regional low-altitude airspace supervision and unmanned aerial vehicle traffic management scenarios. In this case, a continuous global interactive multi-model state for the same aircraft can be maintained in the scenario. The measurement noise is fully statistically analyzed according to the source, and measurement noise adapted to the recent error level of the current source is formed when the source changes. This can reduce track jumps and model probability anomalies caused by cross-source switching, and provide a continuous state basis for unified situation display, airspace capacity analysis, flight conflict early warning and operational status supervision. Alternatively, it can also be applied to target surveillance scenarios around airports and key infrastructure. Alternatively, it can also be applied to cross-platform multi-source track data aggregation and unified state service scenarios. In this case, a unified track state service for reuse by multiple business systems can be built, and audit information such as source evidence quantity, model probability and state can be output. Alternatively, it can also be applied to emergency missions and cloud-edge collaborative low-altitude situation support scenarios, etc.

[0018] Based on the above description, embodiments of the present invention propose an aircraft state estimation method, which can be executed by the aforementioned electronic device (terminal or server); or, the aircraft state estimation method can be executed jointly by the terminal and the server, etc. For ease of explanation, the following description will use the execution of the aircraft state estimation method by an electronic device as an example; such as Figure 1 As shown, the aircraft state estimation method may include the following steps S101-S105: S101, Obtain the current waypoint data of the target aircraft. The current waypoint data includes the current event time, the current field pattern, and the current source identifier. The current source identifier is used to indicate the current data source.

[0019] Optionally, the target aircraft can be any aircraft, and this embodiment of the invention does not limit this. Optionally, the electronic device can use each of the at least one designated aircraft as a target aircraft, and the at least one designated aircraft can be set as needed; or, each monitored aircraft can be used as a target aircraft, and so on. For ease of explanation, the following description uses a single target aircraft as an example. In this embodiment of the invention, a trackpoint data can represent a trackpoint.

[0020] The current waypoint data can be the currently acquired waypoint data. In this embodiment of the invention, a waypoint data can represent a data record of the position and / or motion state of an aircraft at an event time (i.e., event time); for example, a waypoint data may include, but is not limited to, at least two of the following: the target identifier of the corresponding aircraft (also known as the aircraft identifier, which can be used to indicate the corresponding aircraft), the source identifier (also known as the source instance identifier or logical source instance identifier, which can be used to indicate the data source), the event time, the position (such as longitude, latitude, altitude value with reference, etc.), the speed, heading, acceleration, accuracy, mass field, etc., which are not limited in this embodiment of the invention. In this embodiment of the invention, the event time in a waypoint data can be the reporting time of the waypoint indicated by the corresponding waypoint data. Optionally, in other embodiments, the event time in a waypoint data can also be the observation and acquisition time of the corresponding waypoint, etc.; which are not limited in this embodiment of the invention.

[0021] Optionally, a data source (also referred to as a waypoint data source, source instance, or logical source instance, etc.) can be any of the following: an aircraft terminal direct reporting system, a broadcast automatic dependent surveillance receiving or aggregation system, an unmanned aerial vehicle remote identification receiving or aggregation system, an external tracking system such as radar or electro-optical, a radio monitoring track system, and other third-party fusion data platforms. This embodiment of the invention does not limit this. Based on this, this embodiment of the invention can uniquely identify a specific external third-party platform, terminal, base station equipment, etc., through a source identifier.

[0022] Optionally, the current data source can also be referred to as the current source. The current track point data can be the track point record of the current processing round, and the current source can be the data source corresponding to the source identifier carried by the track point record of the current processing round.

[0023] Based on this, embodiments of the present invention can achieve aircraft state estimation through a single streaming waypoint event. A single streaming waypoint event can refer to: a single algorithm processing mainly receiving one target waypoint record (such as one waypoint data of the target aircraft) from one waypoint data source (i.e., a data source); a long-term continuous data stream is generated alternately by multiple data sources according to their respective event times, reporting cycles, and transmission delays. In other words, for the target aircraft, one processing round can process only the currently acquired waypoint data.

[0024] Optionally, the current waypoint data may be obtained in the following ways, including but not limited to: The first acquisition method: The electronic device stores multiple waypoint data of the target aircraft in its own storage space. In this case, the electronic device can sequentially traverse each waypoint data of the target aircraft according to the event time, so as to use the currently traversed waypoint data as the current waypoint data of the target aircraft.

[0025] The second acquisition method: The electronic device can receive the current waypoint data sent by the external base station or data platform to acquire the current waypoint data; in this case, the electronic device can continuously receive the waypoint data of the target aircraft to continuously update the current waypoint data, thereby continuously updating the global estimated track of the target aircraft (also known as the global fusion track, etc.).

[0026] The third acquisition method: The electronic device can receive the current unstandardized waypoint data of the target aircraft, and through the pre-processing data governance module, standardize the current unstandardized waypoint data to obtain the current waypoint data of the target aircraft, thereby achieving the acquisition of the current waypoint data, etc. It should be noted that the pre-processing data governance module can be set according to experience or actual needs, and this embodiment of the invention does not limit it; for example, the pre-processing data governance module may include, but is not limited to, at least one of the following: protocol parsing, data source identification, duplicate record identification, duplicate management of multiple base stations broadcasting the same data, time field legality check, basic target identifier mapping (such as mapping the aircraft identifiers of multiple data sources to the same target aircraft identifier), and obvious invalid record filtering, etc. It should be noted that this embodiment of the invention does not limit the specific content of the current unstandardized waypoint data and the current waypoint data.

[0027] For example, the current waypoint data to be standardized can be represented as Where k can represent the processing round index formed according to the processing order of valid event times (i.e., the kth valid event time, etc., when the current waypoint data is detected to meet the global state candidate update conditions of the target aircraft, the current event time can be used as the candidate kth valid event time). It can represent the target identifier of the target aircraft, s k It can represent the current source identifier, g k raw It can represent the current field pattern (which can be the original field of the current track point; the field pattern can also be called the measurement field pattern, etc.), t k event It can represent the current event time (i.e., the time when the target aircraft reported the data event described by the current waypoint), t k report This can represent the time when the current waypoint data to be standardized was received, y kThis can represent the original set of waypoint values ​​for which the internal coordinates, altitude, units, and field semantics of the embodiments of the present invention have not yet been unified. k raw It can indicate the availability of raw fields and field source markers (used to record whether each field exists, is directly provided from an external source, or is estimated), q k raw It can represent a set of quality metadata (such as accuracy, integrity, latency, track status, and external upstream processing chain markers that can be obtained from the current waypoint), L k This can indicate the source category to which the current data source belongs. Optionally, the accuracy can be the accuracy and error of the position and velocity data values ​​explicitly given by the upstream, such as including standard deviation; the quality metadata set includes quality facts such as field integrity, latency, and protocol legality; the track status can refer to states such as normal tracking, temporary extrapolation, recapture, and quality degradation; the external upstream processing chain marker can include, but is not limited to, at least one of the following: markers such as those indicating that the current track point has undergone base station forwarding or intermediate third-party platform processing (e.g., the external upstream processing chain marker can refer to the terminal device reporting, base station forwarding, and third-party platform processing that the current track point has experienced before reaching the system of this invention); the current track point can be the current track point data to be standardized and the track point indicated by the current track point data. In this embodiment of the invention, a source instance may correspond to a specific data channel, such as a specific external platform or a specific terminal; the source category can be used to describe its data formation method, such as terminal direct reporting track, broadcast reception converged track, single non-cooperative detection system tracking track, external fusion track, etc.; multiple source instances may belong to the same source category, the source category can be used to configure prior measurement noise and compatible source rules, etc., and source instances can be used to save their own independent historical statistical states, etc. Optionally, measurement can refer to the data of observation records after being standardized by coordinates, altitude, time, and field semantics, and then entering the state estimation measurement equation. Optionally, the original set of track point values ​​can be geographic coordinates, local rectangular coordinates, or geocentric coordinates, etc., and this embodiment of the invention does not limit this.

[0028] Accordingly, the current waypoint data can be represented as Among them, g k This can represent the standardized measurement field pattern (i.e., the current field pattern), z k It can represent the standardized measurement vector (i.e., the current measurement vector, such as, but not limited to, at least one of the following: longitude position, latitude position, altitude position (e.g., altitude), ground speed, northward speed, eastward speed, vertical speed, heading, track angle, acceleration, etc.), a kThis can represent standardized field availability markers (i.e., current field availability markers, which can be used to indicate whether each measurement field is available), and so on. Based on this, embodiments of the present invention can standardize measurement field patterns, measurement vectors, and field availability markers according to standardization rules to obtain current waypoint data; optionally, the standardization rules can be set according to experience or actual needs, and embodiments of the present invention do not limit this. Optionally, the minimum fields of a waypoint data include target identifier, source identifier, event time, longitude, latitude, and at least one altitude value with a clear reference. Optional fields may include: ground speed, north, east, and vertical speeds, heading or track angle, acceleration, position or speed accuracy, external platform track quality, data latency, and field integrity. Optionally, if a field does not exist, has ambiguous semantics, or cannot be reliably converted, it can be excluded from the current measurement update through measurement field patterns and measurement models. Optionally, the measurement field pattern in a trackpoint data can be used to indicate the actual set of measurement components contained in the corresponding trackpoint data. For example, the measurement field pattern can include position only, position and horizontal velocity, position and three-dimensional velocity, etc. Different measurement field patterns maintain or map the fully statistical state of the source measurement noise in the corresponding dimension.

[0029] For example, standardization rules may include, but are not limited to, coordinate, altitude, and time standardization. This includes filtering the conversion of external latitude, longitude, and altitude track points to local northeast-northeast coordinates, northeast-northeast-sky coordinates, or geocentric-geocentric coordinates; converting different altitude references to a unified altitude; and excluding vertical fields that cannot be reliably converted from participating in 3D measurement updates. Based on this, electronic devices can standardize the original track point value set according to the current field pattern to obtain the current measurement vector (also called the current measurement). The current measurement vector may include the standardized values ​​of each field in the current field pattern; that is, it can be obtained from the original track point value set y k The process identifies the content of each field to be processed in the current field pattern, and standardizes the content of each field to obtain the current measurement vector.

[0030] Optionally, let the state vector of motion model m at event time k (referring to the aircraft motion state estimated by the filter) be x. m,k =[p N,k ,p E,k ,p D,k ,v N,k ,v E,k ,v D,k ,X m,k ] T ; where p N,k p E,k p D,k These can be represented by north, east, and ground-direction position components, vN,k v E,k v D,k These can be the corresponding velocity components, X m,k Additional states (such as acceleration, turning rate, etc.) determined by a specific motion model can be represented. When different motion models have different internal state dimensions, they can be mapped to a unified common position-velocity state space through a preset state mapping matrix. Optionally, the electronic device can also determine the value of each field in the state vector field based on the original track point value set according to the state vector field corresponding to the motion model m, so as to obtain the state vector of the motion model m at the current event time, and thus obtain the state vector of each motion model at the current event time. Optionally, the state vector field corresponding to a motion model can be set according to experience or actual needs, and this embodiment of the invention does not limit this. Optionally, if the current track point data is obtained from its own storage space or received directly, the electronic device can also obtain the state vector of each motion model at the current event time from the storage space, or receive the state vector of each motion model at the current event time, so as to obtain the state vector of each motion model at the current event time, etc.; this embodiment of the invention does not limit this.

[0031] Correspondingly, for the linear measurement field pattern g k The measurement relationship (i.e., the measurement model) of the motion model m can be shown in Equation 1.1: Formula 1.1 Among them, z k The current number of valid measurements (i.e., the current measurement dimension) can be d. k , The motion model m can be defined in the field pattern g. k The measurement matrix below (which can be determined by the motion model state definition and the currently available measurements (i.e., the measurement field pattern)). This can be the random measurement error corresponding to the current data source and the current field pattern. The measurement noise covariance matrix (which can be simply referred to as measurement noise covariance or measurement noise, etc., and can be a parameter formed by fully statistically estimating and shrinking the state through subsequent source measurement noise in the embodiments of this invention) can be used to represent that the random variable follows a normal distribution (as mentioned above). For example, for position-only measurements (i.e., the measurement field pattern only includes position, such as latitude, longitude, and altitude), the measurement matrix can select the position component in the state vector; or, for position and velocity measurements (i.e., the measurement field pattern includes position and velocity), the position and velocity components in the state vector can be selected simultaneously, and so on. Based on this, a motion model and a measurement matrix corresponding to a field pattern can be used to extract the field content of the field indicated by the field pattern from the corresponding state vector. A motion model and a measurement matrix corresponding to a field pattern can be set according to experience or actual needs, and the embodiments of this invention do not limit this. Optionally, for nonlinear measurement fields such as ground speed and heading, the linear measurement matrix can be replaced with a measurement function. Furthermore, extended Kalman spectroscopy, unscented transformation, or equivalent nonlinear measurement update methods are employed, etc.; however, the embodiments of the present invention do not limit this.

[0032] Based on this, the electronic device can determine the measurement noise covariance matrix and generate random measurement errors according to the measurement noise covariance matrix; then, it can select the measurement vector to be measured in the current field mode from the current state vector, and use the sum of the measurement vector to be measured and the random measurement error as the current measurement vector, and so on. Optionally, in other embodiments, the measurement vector to be measured can also be directly used as the current measurement vector, and the present invention does not limit this.

[0033] S102, when it is detected that the current waypoint data meets the global state candidate update conditions of the target aircraft, the historical global interactive multi-model state of the target aircraft under the previous valid event time is determined, and the historical current source measurement noise fully statistical state of the target aircraft under the previous valid event time, current data source and current field mode is determined; wherein, an event time in the waypoint data used to update the global state of the target aircraft is taken as a valid event time of the target aircraft.

[0034] Optionally, the global state candidate update condition can be set according to experience or actual needs, and this embodiment of the invention does not limit this. For example, if the current event time is greater than the previous valid event time of the target aircraft, it can be determined that the current waypoint data meets the global state candidate update condition of the target aircraft, that is, the global state candidate update condition can refer to the current event time being greater than the previous valid event time of the target aircraft; or, if the time interval between the current event time and the previous valid event time of the target aircraft is greater than a preset time interval threshold, it can be determined that the current waypoint data meets the global state candidate update condition of the target aircraft, that is, the global state candidate update condition can refer to the time interval between the current event time and the previous valid event time of the target aircraft being greater than a preset time interval threshold, and so on; this embodiment of the invention does not limit this. Optionally, the preset time interval threshold can be set according to experience or actual needs, and this embodiment of the invention does not limit this. The previous valid event time for the target aircraft can be the event time of the last waypoint data that has been accepted by the global state estimation main link and formed the global posterior, i.e., the event time of the most recent waypoint data used to update the global state, or the most recent valid event time in the historical update process of the target aircraft's global state, and so on; that is, the previous valid event time can be the most recent historical valid event time relative to the current system time. Optionally, the valid event time can also be called the global update event time or the global event time, etc.

[0035] For example, the time interval between the current event time and the target aircraft's previous valid event time can be as shown in Equation 1.2: Equation 1.2 in, G can represent the time of the previous valid event (i.e., the time of the (k-1)th valid event), and G can represent the global time. For example, when the time interval... t kWhen the value is greater than 0, a new global fused track point can be formed normally and added to the global estimated track of the target aircraft. That is, the current track point can enter the prediction and measurement update normally according to the current event time. After forming a new global posterior state, a new fused track point is generated based on the global posterior state and added to the global track sequence (i.e., the global estimated track). At this time, the global event time can be updated to the current event time. Optionally, when the time interval is less than or equal to 0, it means that the current track point is late or time rollback relative to the already output global target aircraft state (i.e., global state). At this time, the current track point data can be processed according to the pre-governance configuration strategy. Optionally, the pre-governance configuration strategy can be set according to experience or actual needs, and this embodiment of the invention does not limit this. For example, the pre-governance configuration strategy may include, but is not limited to, at least one of the following: refusing current waypoint data from entering the online state estimation main chain and retaining audit records; buffering and reordering events that have not yet been publicly released within a short allowable window (such as a time window with a time interval greater than a preset negative value) (e.g., adding a very small output delay; as long as the delay falls within the window, it can be corrected by only modifying the internal cache); recalculating subsequent states from historical checkpoints during offline reproduction or replay processes, etc. Optionally, the preset negative value can be set according to experience or actual needs, and this embodiment of the invention does not limit this.

[0036] Optionally, a global interactive multi-model state may include the filtered output parameters of each motion model in the motion model set. The filtered output parameters of a motion model may include the posterior motion state, posterior state estimation error covariance, and model posterior probability of the corresponding motion model. Optionally, the motion model set may be set according to experience or actual needs, and this embodiment of the invention does not limit this. For example, the motion model set may include, but is not limited to, uniform motion models, uniform acceleration motion models, and coordinated turning motion models, etc. It should be noted that this embodiment of the invention does not limit the specific number of motion models in the motion model set; for example, the number of motion models in the motion model set may be M, where M may be a positive integer. For example, the historical global interactive multi-model state of the target aircraft at the previous valid event time (which can also be represented as the global interactive multi-model state of the target aircraft at the previous valid event time) As shown in Formula 1.3: Equation 1.3 in, The posterior motion state of motion model m (i.e., the m-th motion model in the set of motion models, m∈[1,M], where m is a positive integer) after measurement updates are completed in the previous valid event (i.e., the previous waypoint data used to update the global state) can include the position, velocity, and optional acceleration or turning rate state components defined by motion model m, thus representing the posterior motion state of motion model m at the time of the previous valid event; P m,k-1|k-1 The corresponding posterior state estimation error covariance can be expressed as the posterior state estimation error covariance of motion model m at the time of the previous valid event; μ m,k-1|k-1 The posterior probability of the model after the measurement update for the previous valid event can be expressed as the posterior probability of the motion model m at the time of the previous valid event, satisfying μ. m,k-1|k-1 ≥0 and ∑ m μ m,k-1|k-1 =1. In this embodiment of the invention, changes in the source identifier do not trigger a global interactive multi-model state re-initialization. It should be noted that this embodiment of the invention does not limit the specific initialization process of the filter output parameters of any motion model; for example, initialization can be performed based on the first valid track point data obtained, such as constructing the initial posterior motion state from the measurement vector, constructing the initial posterior state estimation error covariance based on the external uncertainty, allocating the model posterior probabilities of each motion model according to the motion model set configuration, etc.; optionally, the external uncertainty can be set according to experience or actual needs, or it can be determined based on the reliable position accuracy, velocity accuracy, external covariance, etc.; optionally, the model posterior probability can be allocated by equal probability allocation, or the initial model posterior probability can be allocated based on the prior weight given by the conjugate prior parameters when conjugate prior parameters (i.e., model prior hyperparameters) exist, etc.; this embodiment of the invention does not limit this.

[0037] Optionally, the electronic device may include a global interactive multi-model state estimator to maintain a set of global motion model posterior motion states, posterior state estimation error covariance, and model posterior probabilities for the same aircraft; the global interactive multi-model state is shared when multiple waypoint sources (i.e., data sources) arrive alternately, without establishing a complete motion trajectory for each data source separately.

[0038] Optionally, the fully statistical state of source measurement noise can be constructed and maintained by a combined index of source instance i (i.e., data source i) and field pattern g. The combined index can also be called statistical metadata. Optionally, a source instance is not required to always have only one field pattern. For example, the same data source may provide location and velocity under normal circumstances, and may only provide location under degraded circumstances. Therefore, multiple statistical states such as (i, g1) and (i, g2) can exist at the same time, thereby avoiding the forced mixing of measurement noise statistics with different dimensions and physical semantics.

[0039] Optionally, a sufficient statistical state of source measurement noise may include, but is not limited to, at least one of the following: effective sample evidence quantity, cumulative second moment of mixed prediction innovation, cumulative uncertainty of mixed prediction, time of the most recent effective update event (i.e., the event time of the track point corresponding to the most recent effective update in the corresponding data source and field mode (i.e., the event time in the corresponding track point data), that is, it can be the event time of the most recent track point that is actually written into the source statistical state, that is, after a track point data is used to update the global state, the event time in the corresponding track point data can be updated to the most recent effective update event time in the corresponding data source and field mode), statistical confidence, source quality score (such as reflecting the data integrity, timeliness, reporting accuracy legality and physical legality of the current or most recent effective track point), source health score (which can be the numerical result of source health status and continuous anomaly, continuous recovery evidence, the source health status can be normal, degraded, isolated, recovery, etc., such as the source health status can be mapped to the source health score, etc.), etc.; the embodiments of the present invention do not limit this. Optionally, the state transition of the source health state machine can be triggered by the number of consecutive anomalies, the number of consecutive valid anomalies, the normalized squared innovation, and the source quality threshold, etc.; this embodiment of the invention does not limit this. It should be noted that this embodiment of the invention does not limit the specific calculation method of the source quality score and the source health score, that is, it can be set according to experience or actual needs. In other words, this embodiment of the invention does not limit the specific content of the source quality scoring strategy (which can be used to determine the source quality score) and the source health score calculation strategy (which can be used to determine the source health score), etc. For example, the source quality score can also be the weighted sum of the data integrity score, the timeliness score, and the physical legitimacy score, or the source health score can also be achieved through the cumulative anomaly score, sequential statistical test, or probabilistic health score, etc. Among them, the historical current source measurement noise fully statistical state of the target aircraft under the previous valid event time, the current data source, and the current field mode can also be referred to as the source measurement noise fully statistical state of the target aircraft under the previous valid event time, the current data source, and the current field mode. Based on this, the sufficient statistical state of source measurement noise can refer to a set of parameters such as the amount of valid sample evidence, the cumulative amount of mixed prediction innovation second moment, the cumulative amount of mixed prediction uncertainty, the time of the most recent valid update event, statistical confidence, source quality, and health status, which are stored across processing rounds for a data source and measurement field pattern.Optionally, the effective sample evidence quantity can refer to the equivalent sample quantity of the source measurement noise statistics after event time decay, anomaly gating, and source quality weighting, which can simultaneously express the sufficiency of the sample and the time validity of historical statistics; the mixed prediction uncertainty can refer to the prediction measurement uncertainty formed by the state prediction covariance (i.e., prediction covariance) of each motion model and the dispersion between the prediction measurement means of different motion models, without including the current source measurement noise.

[0040] For example, for data source i and measurement field pattern g, the fully statistical state of source measurement noise of the target aircraft at the k-th valid event time, data source i, and field pattern g (which can also be expressed as data source i, field pattern g, and the k-th valid event time). As shown in Formula 1.4: Equation 1.4 Where, N i,g,k A can represent the number of valid sample evidences (values ​​greater than zero, which can be the equivalent sample size after robust weighting and time decay) under the k-th valid event time, data source i, and field pattern g. i,g,k B can represent the cumulative second-order moment of the mixed predicted innovation under the k-th valid event time, data source i, and field pattern g. i,g,k This can be represented by the cumulative mixed prediction uncertainty under the k-th valid event time, data source i, and field pattern g. The cumulative second moment of the mixed prediction innovation and the cumulative mixed prediction uncertainty can both be d. g ×d g The matrix, d gThis can be the measurement dimension under field pattern g. Correspondingly, the 4th to 7th fields from left to right within the brackets in Formula 1.4 can represent, respectively, the kth valid event time, the most recent valid update event time under data source i and field pattern g (i.e., the most recent trackpoint event time that was actually written into the corresponding source measurement noise sufficient statistical state under data source i and field pattern g; that is, the event time of the most recent trackpoint data that updated the source measurement noise sufficient statistical state under data source i and field pattern g); the statistical confidence level under data source i, field pattern g, and the kth valid event time; the source quality score under data source i and the kth valid event time; and the source health score under data source i and the kth valid event time. Optionally, the statistical confidence level, source quality score, and source health score can all be within [0,1]. As can be seen, the embodiments of the present invention can save the cumulative amount of the second moment of the mixed prediction innovation, the cumulative amount of the mixed prediction uncertainty, and the amount of effective sample evidence for each data source and field pattern, so that the increase of error of a certain source (i.e., data source) will not directly cover the historical statistics of other sources, thus preventing the measurement error statistics of different sources from overlapping each other. When the source reappears, its own statistical evidence can be restored, so as to effectively solve the problem of mixing errors from different sources in a single global adaptive window.

[0041] Optionally, when the field pattern g of data source i appears for the first time, the effective sample evidence quantity, the cumulative second moment of mixed prediction innovation, and the cumulative uncertainty of mixed prediction can be initialized to 0, such as N. i,g,0 =0, A i,g,0 =0, B i,g,0 =0; Correspondingly, source category priors can also be configured (such as the basic measurement noise covariance pre-configured according to data formation method and field pattern, different source categories can have different priors), such as source category L i The prior measurement noise covariance (i.e., the basic measurement noise covariance) corresponding to field pattern g can be expressed as: Optionally, the prior measurement noise covariance corresponding to a source category and a field pattern can be set according to experience or actual needs, and this embodiment of the invention does not limit this; for example, it can be formed from historical data, offline sample statistics, or conservative engineering scope, etc.

[0042] Based on this, the prior measurement noise covariance corresponding to the source category of any data source and the field pattern of any data source can be used as the prior measurement noise covariance of any data source and any field pattern. Optionally, when the track point data of the target aircraft appears for the first time for any field pattern of any data source, the prior equivalent evidence quantity corresponding to the source category of any data source and any field pattern can also be determined as the prior equivalent evidence quantity of any data source and any field pattern, and so on. For example, source category L i The prior equivalent evidence quantity corresponding to field pattern g can be expressed as: The prior equivalent evidence quantity can be a dimensionless prior equivalent sample evidence quantity, which can be used to control the strength of the prior relative to online statistical evidence. Optionally, the prior equivalent evidence quantity can be any value greater than 0, that is, the prior equivalent evidence quantity corresponding to a source category and a field pattern can be set according to experience or actual needs, and the embodiments of the present invention do not limit this. For example, the prior equivalent evidence quantity can be configured by the prior reliability. The more sufficient the prior evidence, the larger the value. When there is a lack of reliable calibration, a smaller positive value can be used so that the online valid samples can quickly replace the prior, and so on.

[0043] Optionally, when the electronic device lacks prior knowledge of the source category, for field pattern g, the system conservative prior measurement noise covariance corresponding to field pattern g can be used. In this case, the system conservative prior measurement noise covariance and system conservative prior equivalent evidence quantity corresponding to any field pattern can be used as the prior measurement noise covariance and prior equivalent evidence quantity for any data source and any field pattern, and so on. Optionally, the system conservative prior measurement noise covariance and system conservative prior equivalent evidence quantity corresponding to a field pattern can be set according to experience or actual needs, and the embodiments of the present invention do not limit this.

[0044] S103, based on the current track point data and the full statistical status of the historical current source measurement noise, determine the historical measurement noise of the current source; and based on the historical measurement noise of the current source, determine the applicable measurement noise of the current source.

[0045] Optionally, a sufficient statistical state of a source measurement noise may include, but is not limited to, at least one model metric parameter and the time of the most recent valid update event; optionally, at least one model metric parameter may include, but is not limited to, the amount of valid sample evidence, the cumulative amount of the second moment of the mixed prediction innovation, and the cumulative amount of the mixed prediction uncertainty.

[0046] It should be noted that the specific parameters of at least one model metric parameter in the embodiments of the present invention are not limited. For example, it may include any parameter equivalent to the effective sample evidence quantity, the cumulative second moment of the mixed prediction innovation, and the cumulative mixed prediction uncertainty. That is, it may also include any parameter capable of recovering the two core information types: evidence strength and measurement second-order statistics (i.e., the outer product of the mixed prediction innovation and the cumulative second moment of the mixed prediction innovation) minus prediction uncertainty (i.e., the second-order statistical information AB after deducting prediction uncertainty). For example, in other embodiments, at least one model metric parameter may also include conjugate prior parameters such as prior degrees of freedom and scale matrix to express covariance statistical evidence, etc.; or it may include a weighted second moment with a forgetting factor, etc.; the present invention does not limit this. For ease of explanation, the following description will use at least one model metric parameter including the effective sample evidence quantity, the cumulative second moment of the mixed prediction innovation, and the cumulative mixed prediction uncertainty as examples.

[0047] Optionally, when determining the historical measurement noise of the current source based on the current trackpoint data and the sufficient statistical state of the historical current source measurement noise, the most recent valid update event time of the current source can be determined from the sufficient statistical state of the historical current source measurement noise. This most recent valid update event time in the sufficient statistical state of the historical current source measurement noise can be used as the most recent valid update event time of the current source. Based on the difference between the current event time and the most recent valid update event time of the current source, a current source attenuation factor (also known as the attenuation factor of the target aircraft under the current data source, current field mode, and current event time) can be determined. Then, the model measurement parameters in the sufficient statistical state of the historical current source measurement noise can be attenuated according to the current source attenuation factor to obtain at least one attenuated model measurement parameter. Further, the prior measurement noise covariance under the current data source and current field mode can be determined, and based on this prior measurement noise covariance and at least one attenuated model measurement parameter, the historical measurement noise of the current source can be determined.

[0048] Optionally, the decay factor for data source i, field pattern g, and current event time (which can be the kth valid event time in this case) can be as shown in Formula 1.5: Formula 1.5 Where, ρ i,g,k It can represent the data source i, field pattern g, and decay factor under the current event time, k-1 can represent the previous valid event time (also known as the (k-1)th valid event time), λ i,gThe difference in Formula 1.5 can represent the statistical evidence time decay rate under data source i and field pattern g. Therefore, when the current data source is data source i and the current field pattern is field pattern g, the difference can represent the difference between the current event time and the time of the most recent valid update event from the current source. It can indicate the current event time. This can represent the data source i, field pattern g, and the most recent valid update event time under the (k-1)th valid event time; exp(.) can represent an exponential function, and max(.) can represent a function that takes the maximum value. Optionally, the statistical evidence time decay rate for a data source and a field pattern can be set according to experience or actual needs, or it can be configured through the expected half-life of the corresponding data source and the corresponding field pattern (such as in the case of...). ), etc.; the embodiments of the present invention do not limit this. Wherein, T 1 / 2,i,g It can represent the expected half-life under data source i and field pattern g; optionally, the expected half-life under a data source and a field pattern can be set according to experience or actual needs, and the embodiments of the present invention do not limit this.

[0049] For example, for data source i and field pattern g, at least one decay model metric (also known as a decayed statistic, which may include the decay model metric of each of the at least one model metric) can be as shown in Equation 1.6: Equation 1.6 In Equation 1.6, the parameters on the left-hand side can represent, in turn, the attenuated effective sample evidence quantity (also known as attenuated effective sample evidence quantity), the attenuated cumulative second moment of mixed prediction innovation (also known as attenuated cumulative second moment of mixed prediction innovation), and the attenuated cumulative mixed prediction uncertainty (also known as attenuated cumulative mixed prediction uncertainty); N i,g,k-1 A i,g,k-1 B i,g,k-1These can represent the effective sample evidence quantity, the cumulative second moment of mixed prediction innovation, and the cumulative mixed prediction uncertainty of the target aircraft under the previous effective event time, data source i, and field pattern g (or data source i, field pattern g, and the (k-1)th effective event time, etc.), where k is a positive integer. The order of parameter subscripts is not limited in this embodiment of the invention. Optionally, for sources that did not actually arrive in the current round, it is not necessary to write the attenuation result for each round. Instead, when the data source is used again as the current data source or an external reference, the above effective statistics (i.e., the attenuated statistics) can be calculated based on the most recent effective update event time stored therein, to avoid repeated attenuation for the same time interval. In this embodiment of the invention, what is attenuated is the statistical evidence strength (i.e., the effective sample evidence quantity), rather than simultaneously modifying the measurement noise estimate and reducing its reference weight again, thus avoiding repeated inclusion of time factors. As can be seen, the embodiments of the present invention can apply the same event time decay (i.e., decay according to the same decay factor) to the effective sample evidence quantity and its corresponding second-order moment cumulant. It should be understood that as the source has not been updated for a long time, the effective sample evidence quantity naturally decreases, and the constraint effect of the historical covariance estimate on the current parameters weakens accordingly. At the same time, the historical covariance estimate itself will not be repeatedly modified simply because of the passage of time. This avoids the double weighting of "time decay" directly affecting the covariance on the one hand and repeatedly affecting the fusion weight on the other hand (if the source historical measurement noise is directly decayed to the prior, and the fusion weight of the source historical measurement noise is reduced at the same time, the time factor will be repeatedly included). Therefore, the embodiments of the present invention decay N, A, and B proportionally, so that the statistical point estimate of the source historical measurement noise maintains its existing structure when there is no new evidence, but its constraint strength in this round of statistical contraction decreases with N. This reflects the aging of history and avoids the unfounded repeated modification of the covariance, so as to achieve the problem of insufficient samples and historical failure by unifying the effective sample evidence quantity.

[0050] Furthermore, when determining the historical measurement noise of the current source based on the prior measurement noise covariance and at least one attenuation model metric parameter, the electronic device can also determine the prior equivalent evidence quantity for the current data source and the current field pattern. Then, using this prior equivalent evidence quantity, the prior measurement noise covariance, and at least one attenuation model metric parameter, the historical measurement noise of the current source is determined (which can also be referred to as the historical measurement noise of the target aircraft at the current data source, current field pattern, and current event time, or the historical measurement noise of the current data source and current field pattern at the current event time, etc.). For example, the historical measurement noise of data source i and field pattern g at the current event time (i.e., the historical measurement noise of the source). As shown in Formula 1.7: Equation 1.7 Here, the source historical measurement noise can be an estimate of the source historical measurement noise formed by historical online statistical evidence and priors (which can be the covariance matrix in the measurement space, i.e., d). g ×d g The matrix, d g (can represent the measurement dimension corresponding to field pattern g), κ i,g,0 This can represent the amount of prior equivalent evidence under data source i and field pattern g. This can represent the prior measurement noise covariance under data source i and field pattern g. Optionally, for field pattern g, the j-th diagonal element in a measurement noise (such as historical measurement noise from the source) can represent the variance of the j-th measurement channel error in field pattern g, and the off-diagonal elements can represent the cross covariance between the errors of two measurement channels. For example, in the three-dimensional position mode, this can correspond to the variances of the north, east, and ground position errors and their mutual cross covariances, etc. The measurement noise covariance constraint operator corresponding to field pattern g can constrain the original matrix within the parentheses. Optionally, the measurement noise covariance constraint operator corresponding to a field pattern can be set according to experience or actual needs. The measurement noise covariance constraint operators corresponding to different field patterns can be the same or different, and this embodiment of the invention does not limit this. For example, a measurement noise covariance constraint operator may include, but is not limited to, at least one of the following: symmetry (which can obtain (R+R) T (R can represent the original matrix). Through eigenvalue decomposition, negative or excessively small eigenvalues ​​are restricted to a preset positive lower bound; excessively large eigenvalues ​​are restricted to the upper bound corresponding to the field pattern; physically reasonable variance ranges are set according to measurement channels (such as position, height, velocity, etc.); when necessary, cross-covariances that do not meet the condition that the absolute value of the correlation coefficient is not greater than 1 are constrained; when matrix inversion is required for state updates, the minimum eigenvalue or diagonal regularization term ensures positive definiteness, etc. Correspondingly, the preset positive lower bound, the upper bound corresponding to a field pattern, and the variance range corresponding to each measurement channel can all be set according to experience or actual needs, and this embodiment of the invention does not limit this. Based on this, this embodiment of the invention can ensure that the output matrix can be used as the subsequent filtered measurement noise covariance through the measurement noise covariance constraint operator, but it is not used to cover up persistent statistical model errors; if the original matrix frequently triggers large projections, the statistical reliability should be reduced or anomaly management should be implemented. Optionally, a source historical measurement noise can refer to the measurement noise covariance estimate formed based on the sufficiently statistical state after attenuation of a data source and the source prior. As can be seen, the embodiments of the present invention can achieve prior shrinkage of source categories and matrix validity constraints through Formula 1.7.

[0051] Optionally, the electronic device can also determine the statistical confidence level of the target aircraft under the current data source, current field mode, and current event time, to be added to the sufficient statistical state of the source measurement noise of the target aircraft under the current data source, current field mode, and current event time. The statistical confidence level under a data source, a field mode, and a valid event time can be used to reflect whether the historical measurement noise of the corresponding data source, field mode, and valid event time is formed by sufficient and stable historical evidence (such as the amount of valid sample evidence N, the cumulative amount of the second moment of mixed prediction innovation A, the cumulative amount of mixed prediction uncertainty B, related anomalies, constrained projection records, etc.). Optionally, the statistical confidence level of the target aircraft under the current data source, current field mode, and current event time can be determined based on the score of at least one statistical evaluation indicator of the target aircraft under the current data source, current field mode, and current event time. The score of at least one statistical evaluation indicator can include the scores (also called fractions) of each statistical evaluation indicator among at least one statistical evaluation indicator. Optionally, at least one statistical evaluation indicator can be set according to experience or actual needs, and this embodiment of the invention does not limit this. For example, at least one statistical evaluation indicator may include, but is not limited to, at least one of the following: anomaly trackpoint evaluation indicator, trigger feature value or channel upper and lower bound significant truncation number indicator, and number of times excessive relative changes occur, etc.

[0052] For example, taking data source i, field pattern g, and current event time as an example, and using at least one statistical evaluation indicator including an abnormal track point evaluation indicator, a trigger feature value or channel upper and lower bound significant truncation number indicator, and a number of excessive relative changes evaluation indicator, the score of at least one statistical evaluation indicator can be shown in Formula 1.8: Formula 1.8 in, This can be defined as the number of abnormal track points within the evaluation window (also known as the recent statistical evaluation window, etc., which can be set according to experience or actual needs; for example, the evaluation window can be a window with a duration equal to the evaluation window duration and an end time equal to the current event time; the evaluation window duration can be set according to experience or actual needs) of data source i, field mode g, and the current event time. (This can be determined by information statistics gating or physical boundaries, and can be defined as the number of abnormal track points whose event time is within the evaluation window of data source i and field mode g.) This can be defined as the number of times that the candidate matrix of original measurement noise (i.e., the original matrix mentioned above) within the evaluation window under the current event time, triggers significant truncation of eigenvalues ​​or channel upper and lower bounds when forming historical measurement noise from the source i, field pattern g, and the source i, triggers significant truncation of eigenvalues ​​or channel upper and lower bounds. This can be defined as the number of times the candidate matrix of the original measurement noise within the evaluation window at the current event time has undergone excessive relative change compared to the previous stable estimate (i.e., the source historical measurement noise at the previous valid event time) (i.e., the number of unstable events); correspondingly, the left side of equation 1.8 can represent, from top to bottom, the scores under the abnormal track point evaluation index (also known as the proportion of abnormal track points within the most recent statistical evaluation window), the scores under the trigger feature value or channel upper and lower bound significant truncation number index (also known as the boundary projection trigger ratio within the most recent statistical evaluation window), and the scores under the number of excessive relative changes evaluation index (also known as the statistical instability ratio within the most recent statistical evaluation window), such that each value can belong to [0, 1]; W i,g This can represent the number of candidate update events within the most recent statistical evaluation window under data source i and field mode g. Candidate update events can refer to trackpoint events that have completed mixed prediction innovation calculations and meet the conditions for determining whether the corresponding trackpoint can be written into the sufficiently statistical state of the source measurement noise (i.e., trackpoint events indicated by the trackpoint data). For example, unstable events can be judged by the change in the Frobenius norm of the original measurement noise candidate matrix relative to the previous stable estimate exceeding a threshold, as shown in Equation 1.9: Equation 1.9 in, This can represent the candidate matrix of raw measurement noise at data source i, field pattern g, and the j-th valid event time. This can represent the corresponding previous stable estimate, ||.|| F The Frobenius norm can be represented, ε can be a parameter used to prevent the denominator from being zero (which can be set according to experience or practical needs), and τ R This can be a stability threshold (which can be set based on experience or actual needs). Based on this, if the left side of the inequality in Equation 1.9 is greater than the stability threshold, it can be determined as an unstable event, and so on.

[0053] In this case, the statistical confidence level of the target aircraft under data source i, field pattern g, and current event time. As shown in Formula 1.10: Formula 1.10 Among them, w o The weights can be configured for the evaluation indicators of abnormal waypoints, w b The weights can be configured for the number of times the trigger feature value or channel upper and lower bounds are significantly truncated, w uThe weights for the evaluation index can be configured for the number of times a large relative change occurs. clip(.) can represent a truncation operator (which can be used to force the value to be constrained within the upper and lower limits, such as constraining it to [0,1] as mentioned above). Optionally, the weights for each evaluation index can be set according to experience or actual needs, and this embodiment of the invention does not limit this.

[0054] S104, based on the applicable measurement noise from the current source and the historical global interactive multi-model state, determines the current global interactive multi-model state of the target aircraft at the current event time; and based on the current global interactive multi-model state, determines the current global state of the target aircraft at the current event time; the current global state supports the updating of the global estimated track of the target aircraft.

[0055] Optionally, when the current waypoint data is detected to meet the global state update conditions of the target aircraft, the current event time can be used as the current valid event time (or the kth valid event time), thereby triggering the execution of the above-mentioned determination of the target aircraft's current global interactive multi-model state at the current event time based on the applicable measurement noise from the current source and the historical global interactive multi-model state, thus obtaining the target aircraft's global interactive multi-model state at the kth valid event time, and so on. Optionally, in other embodiments, the current event time can also be directly used as the current valid event time, thereby triggering the execution of the above-mentioned determination of the target aircraft's current global interactive multi-model state at the current event time based on the applicable measurement noise from the current source and the historical global interactive multi-model state, and so on; the present invention does not limit this.

[0056] Optionally, the global state update condition can be set according to experience or actual needs, and this embodiment of the invention does not limit this. Optionally, the global state update condition can be the same as or different from the global state candidate update condition, and this embodiment of the invention does not limit this. For example, the global state update condition can be that the waypoint has not been abnormally rejected (in this case, it can be determined that the current waypoint data meets the global state update condition of the target aircraft when it is determined that the current waypoint has not been abnormally rejected), such as when the normalized mixture innovation square is less than or equal to the abnormal rejection threshold, it can be determined that the waypoint has not been abnormally rejected, etc. Optionally, the abnormal rejection threshold can be set according to experience or actual needs, and this embodiment of the invention does not limit this.

[0057] Optionally, the current global interactive multi-model state of the target aircraft at the current event time can also be referred to as the global interactive multi-model state of the target aircraft at the current event time.

[0058] S105, based on the applicable source measurement noise, determine the sufficient statistical state of the source measurement noise of the target aircraft at the current event time, so as to use it for the state estimation of the target aircraft at the next valid event time.

[0059] Optionally, the fully statistical status of the source measurement noise of the target aircraft at the current event time may include the fully statistical status of the source measurement noise of the target aircraft at the current event time (which may be the current valid event time) across multiple statistical metadata. Optionally, the multiple statistical metadata may include statistical metadata composed of the current data source and the current field mode. That is, the fully statistical status of the source measurement noise of the target aircraft at the current event time may include the fully statistical status of the source measurement noise of the target aircraft at the current data source, the current field mode, and the current event time.

[0060] Optionally, when determining the sufficient statistical state of source measurement noise of the target aircraft at the current event time based on the applicable measurement noise of the current source, the sufficient statistical state of source measurement noise of the target aircraft at the current data source, current field mode, and current event time can be determined based on the applicable measurement noise of the current source. For any statistical metadata other than the statistical metadata composed of the current data source and the current field mode among multiple statistical metadata, the sufficient statistical state of source measurement noise of the target aircraft at any statistical metadata and the previous valid event time can be used as the sufficient statistical state of source measurement noise of the target aircraft at any statistical metadata and the current event time, and / or determine the data source of the target aircraft in any statistical metadata and the source health score at the current event time, so as to update the source health score of the target aircraft in the sufficient statistical state of source measurement noise of any statistical metadata and the current event time, etc.; the embodiments of the present invention do not limit this. The specific implementation of determining the sufficient statistical state of source measurement noise of the target aircraft at the current data source, current field mode, and current event time based on the applicable measurement noise of the current source can be seen below, and the embodiments of the present invention will not be repeated here. Based on this, the source measurement noise of the target aircraft can be fully statistically analyzed based on the source measurement noise of the target aircraft under any statistical metadata and the previous valid event time. This allows the source measurement noise of the target aircraft under any statistical metadata and the current event time to continue using multiple values ​​from the source measurement noise fully statistically analyzed state of the target aircraft under any statistical metadata and the previous valid event time, and so on.

[0061] Optionally, the electronic device may also output the aircraft state estimation data to be output at the current event time; optionally, the aircraft state estimation data to be output may include, but is not limited to, at least one of the following: newly added fused track points indicated by the current global state, finite-time domain predicted track, current global state, current data source identifier, source type, current field pattern, current source historical measurement noise, source external reference matrix, current source applicable measurement noise, each equivalent evidence quantity, each motion model prediction probability, reason for not updating, source quality score, source health status, degraded, isolated and recovered status, etc.; the embodiments of the present invention do not limit this.

[0062] Optionally, in each processing round, at least the following can be saved: the global interactive multi-model state at the current valid event time; the source measurement noise sufficient statistics state at the current valid event time, including the data sources, field patterns, and the source measurement noise; the current valid event time; the source category prior, the system conservative prior, and the configuration version; the current valid prediction deadline, which is the latest event time allowed to continue outputting prediction results based on the current global posterior, according to the maximum prediction time domain and uncertainty constraints; the process noise adaptive state of each motion model (such as persistent mismatch evidence and baseline process noise covariance, etc.); this embodiment of the invention does not limit this. Optionally, the state (such as the global interactive multi-model state, the source measurement noise sufficient statistics state, etc.) can be saved in a database, key-value state storage, a stateful stream processing framework, or a structured binary object; this embodiment of the invention does not limit this. Optionally, the state saving should at least be able to restore the previous valid global interactive multi-model state, the source measurement noise sufficient statistics state, etc., according to the target identifier, source identifier, etc.

[0063] In summary, as long as each external source can continuously output trackpoint data with source identifiers and event times, the embodiments of the present invention can form a fully statistical state of source measurement noise across the wheel, which is suitable for the actual data form of low-altitude situational awareness systems accessing multiple types of external trackpoints. Therefore, it does not need to rely on the original sensor measurements at the same time. In other words, the embodiments of the present invention can achieve aircraft state estimation without acquiring the original measurements of multiple sensors at the same time. Furthermore, the embodiments of the present invention can maintain a unified track continuity without maintaining the complete motion state of each source. That is, the embodiments of the present invention can maintain only one set of global interactive multi-model motion states for the same aircraft. That is, what is continuously inherited is the posterior state vector, posterior covariance, and posterior probability of each motion model. Only the fully statistical state of source measurement noise related to the measurement error statistics of each data source can be saved, thereby reducing the problems of state staleness, time registration, and repetitive processing of common priors caused by long-term lack of observation of multiple sets of local states, and effectively reducing the risk of source changes being misjudged as aircraft maneuvers.

[0064] This invention provides embodiments that can acquire current waypoint data of a target aircraft. The current waypoint data includes the current event time, current field mode, and current source identifier, with the current source identifier indicating the current data source. When the current waypoint data is detected to meet the global state candidate update conditions of the target aircraft, the historical global interactive multi-model state of the target aircraft under the previous valid event time is determined, as well as the historical current source measurement noise fully statistical state of the target aircraft under the previous valid event time, current data source, and current field mode is determined. An event time in the waypoint data used to update the global state of the target aircraft is considered a valid event time for the target aircraft. Then, based on the current waypoint data and the historical current source measurement noise fully statistical state, the historical measurement noise of the current source can be determined; and based on the historical measurement noise of the current source, the applicable measurement noise for the current source can be determined. Furthermore, based on the applicable measurement noise from the current source and the historical global interactive multi-model state, the current global interactive multi-model state of the target aircraft at the current event time can be determined; and based on the current global interactive multi-model state, the current global state of the target aircraft at the current event time can be determined; the current global state supports the updating of the global estimated track of the target aircraft. Correspondingly, based on the applicable measurement noise from the current source, a sufficiently statistical state of the source measurement noise of the target aircraft at the current event time can be determined for use in the state estimation of the target aircraft at the next valid event time. It is evident that the embodiments of the present invention can determine the historical measurement noise from the current source through current track point data and the sufficiently statistical state of the current source measurement noise, thereby determining the applicable measurement noise from the current source with higher accuracy. Furthermore, the applicable measurement noise from the current source and the historical global interactive multi-model state can be used to determine the current global state with higher accuracy, effectively improving the accuracy of aircraft state estimation.

[0065] Based on the above description, this embodiment of the invention also proposes a more specific aircraft state estimation method. Accordingly, this aircraft state estimation method can be executed by the aforementioned electronic device (terminal or server); or, the aircraft state estimation method can be executed jointly by the terminal and the server, and so on. For ease of explanation, the following description will use the execution of the aircraft state estimation method by an electronic device as an example; please refer to [link to relevant documentation]. Figure 2 The aircraft state estimation method may include the following steps S201-S209: S201, Obtain the current waypoint data of the target aircraft. The current waypoint data includes the current event time, the current field pattern, and the current source identifier. The current source identifier is used to indicate the current data source.

[0066] S202, when it is detected that the current waypoint data meets the global state candidate update conditions of the target aircraft, determine the historical global interactive multi-model state of the target aircraft under the previous valid event time, and determine the historical current source measurement noise fully statistical state of the target aircraft under the previous valid event time, current data source and current field mode.

[0067] S203, based on the current track point data and the full statistical status of the historical current source measurement noise, determine the historical measurement noise of the current source; and based on the historical measurement noise of the current source, determine the applicable measurement noise of the current source.

[0068] Optionally, when determining the applicable measurement noise for the current source based on the historical measurement noise of the current source, the electronic device can determine the set of compatible source statistical metadata corresponding to the current statistical metadata composed of the current data source and the current field pattern. A data source in a set of compatible source statistical metadata is a data source other than the current data source in the source set. Based on the sufficient statistical state of the source measurement noise under each compatible source statistical metadata in the previous valid event time and the set of compatible source statistical metadata, the electronic device can determine the historical measurement noise of the target aircraft under the current event time and each compatible source statistical metadata. Therefore, based on the historical measurement noise of the target aircraft under the current event time and each compatible source statistical metadata, an external reference matrix is ​​determined. And based on the external reference matrix and the historical measurement noise of the current source, the applicable measurement noise for the current source is determined. Optionally, the source set may include all data sources corresponding to the obtained track point data of the target aircraft, or all healthy sources corresponding to the target aircraft (i.e., data sources with a healthy source health status), etc.; this embodiment of the invention does not limit this.

[0069] Optionally, the compatible source statistical metadata set may include all statistical metadata from any data source other than the current data source in the source set. For ease of explanation, the current data source will be referred to as s below. k And the current field mode is g k Let's take an example. For instance, a compatible source statistical metadata set can be represented as shown in Formula 2.1: Equation 2.1 Wherein, the left side of the equation can represent the set of compatible source statistical metadata, (i,g i () can represent the data source i and the field pattern g iThe statistical metadata constituted by this function, `compatible`, can represent a compatibility necessity condition judgment function (i.e., if the compatibility necessity condition is met, the value can be 1, thus the corresponding statistical metadata with a value of 1 can be used as the compatibility source statistical metadata). It should be noted that the specific content of the compatibility necessity condition is not limited in this embodiment of the invention; for example, the compatibility necessity condition may include, but is not limited to, at least one of the following: measurement semantic compatibility (i.e., the currently required measurement in the field pattern g of data source i). i The data must have the same physical meaning, or be reliably mapped through transformation; be semantically compatible in terms of coordinates and height (belonging to the same coordinate and height reference after standardization); be compatible in terms of source category (e.g., the source category to which the data belongs is an allowed reference source category of the current data source category, and the allowed reference source categories of each source category can be set according to experience or actual needs); and be statistically valid (i.e.,...). , Can represent field pattern g i The minimum effective sample evidence size can be set according to experience or actual needs, and the source must be healthy and valid (e.g., the source is not in an isolated state and the source health score is greater than 0), etc.; this embodiment of the invention does not limit this. Optionally, reliable mapping can refer to obtaining field data with the same physical meaning as the missing field through reliable transformation or mapping.

[0070] Optionally, the electronic device can also perform data source deduplication processing on the compatible source statistical metadata set, thereby updating the compatible source statistical metadata set so that the data sources in different compatible source statistical metadata in the compatible source statistical metadata set are different. Optionally, for any compatible data source in the compatible source statistical metadata set, if there are multiple compatible source statistical metadata containing any compatible data source, the best compatible field pattern can be selected from the compatible source statistical metadata containing any compatible data source, and the compatible source statistical metadata composed of any compatible data source and the best compatible field pattern can be retained, and all compatible source statistical metadata composed of any compatible data source and field patterns other than the best compatible field pattern can be deleted from the compatible source statistical metadata set; or, one compatible source statistical metadata containing any compatible data source can be randomly retained, etc.; the embodiments of the present invention do not limit this. Optionally, the best compatible field pattern can refer to the field pattern that is most similar to the current field pattern (such as having the fewest transformation processes, etc.); the embodiments of the present invention do not limit the specific method of determining the best compatible field pattern.

[0071] Optionally, when determining the source historical measurement noise of the target aircraft under the current event time and various compatible source statistical metadata based on the sufficient statistical state of the source measurement noise of the target aircraft under each compatible source statistical metadata in the previous valid event time and the compatible source statistical metadata set, for any compatible source statistical metadata in the compatible source statistical metadata set, the source historical measurement noise of the target aircraft under the current event time and any compatible source statistical metadata can be determined according to the method indicated by Formulas 1.5-1.7 above, based on the sufficient statistical state of the source measurement noise of the target aircraft under the previous valid event time and any compatible source statistical metadata; that is, the method for determining the source historical measurement noise of the target aircraft under the current event time and any compatible source statistical metadata can be the same as the method for determining the current source historical measurement noise, and this embodiment of the present invention will not be repeated here.

[0072] Optionally, when determining the source-external reference matrix based on the source historical measurement noise of the target aircraft under the current event time and various compatible source statistical metadata, the electronic device can determine the reference evidence weights of each compatible source statistical metadata under the current event time, and perform dimensional mapping transformation on the source historical measurement noise of the target aircraft under the current event time and various compatible source statistical metadata according to the current field mode, to obtain the source historical measurement noise transformation result of the target aircraft under the current event time and various compatible source statistical metadata, thereby mapping the source historical measurement noise here to a covariance matrix adapted to the current field mode. Optionally, the dimensional mapping transformation between any two field modes can be set according to experience or actual needs, such as being determined based on the correspondence and coordinate transformation between the two field modes, which is not limited in this embodiment of the invention. Further, the source-external reference matrix can be determined based on the reference evidence weights of each compatible source statistical metadata under the current event time and the source historical measurement noise transformation result of the target aircraft under the current event time and various compatible source statistical metadata.

[0073] For example, compatible source statistical metadata (i,g) i The weight of reference evidence at the current event time (i.e., data source i, field pattern g) i (and the weight of reference evidence at the current event time) As shown in Formula 2.2: Equation 2.2 Optional, This could be taking the most recent valid source quality score from data source i (not the current data source at this point) (e.g., the target aircraft's quality score from data source i, field pattern g). iAnd the source quality score, which includes a fully statistical state of source measurement noise at the previous valid event time. The time aging has been determined through... This reflects the event time decay.

[0074] For example, the target aircraft's statistical metadata (i, g) at the current event time and compatible sources. i Source historical measurement noise transformation results As shown in Formula 2.3: Equation 2.3 in, This can be used to specify the data source i and field pattern g. i The dimension mapping transformation matrix maps the source historical measurement noise to the source historical measurement noise transformation result in the current field mode. Thus, the dimension mapping transformation can be achieved through this dimension mapping transformation matrix. This dimension mapping transformation matrix can be determined according to the correspondence and coordinate transformation between the two field modes (that is, the dimension mapping transformation matrix between any two field modes can be set according to experience or actual needs). The implementation of this invention does not limit this. It should be understood that if the coordinates and units have been unified, then T often degenerates into an identity matrix, selection matrix or permutation matrix to achieve the dimension mapping transformation.

[0075] Optional, external reference matrix As shown in Formula 2.4: Equation 2.4 Among them, -s k This can indicate excluding the current data source s k It should be noted that the embodiments of the present invention do not limit the specific method for determining the external reference matrix; for example, the external reference matrix can also be generated based on the source historical measurement noise transformation results of the target aircraft under the current event time and various compatible source statistical metadata, etc., through robust matrix mean or hierarchical statistical methods with clear a priori interpretation.

[0076] Optionally, if no valid compatible source exists, i.e., the set of compatible source statistical metadata is empty, the external reference matrix can degenerate into the current source category prior or the system conservative prior. For example, when the prior measurement noise covariance corresponding to the current data source and the current field pattern exists, the prior measurement noise covariance corresponding to the current data source and the current field pattern can be used as the external reference matrix; conversely, when the prior measurement noise covariance corresponding to the current data source and the current field pattern does not exist, the system conservative prior measurement noise covariance corresponding to the current field pattern can be used as the external reference matrix, and so on. The external reference matrix can also be called the external measurement noise reference matrix or external measurement noise, etc.

[0077] Optionally, the current trackpoint data also includes external measurement uncertainty parameters. Based on this, when determining the applicable measurement noise for the current source based on the external reference matrix and the historical measurement noise of the current source, the current accompanying accuracy matrix can be determined based on the external measurement uncertainty parameters. The applicable measurement noise for the current source can be determined based on the current accompanying accuracy matrix, the external reference matrix, and the historical measurement noise of the current source. Optionally, the external measurement uncertainty parameters may include, but are not limited to, at least one of the following: reliable position accuracy, velocity accuracy, and external covariance, etc., and this embodiment of the invention does not limit these parameters.

[0078] Optionally, when determining the current reporting accuracy matrix based on external measurement uncertainty parameters, if the external measurement uncertainty parameters include external covariance (i.e., the upstream directly provides the covariance matrix of the current measurement), the external covariance after unit and coordinate transformation can be used as the current reporting accuracy matrix; or, if the external measurement uncertainty parameters include reliable position accuracy and velocity accuracy, the position variance and velocity variance can be determined based on the reliable position accuracy and velocity accuracy respectively, and the current reporting accuracy matrix can be constructed using the position variance and velocity variance. The current reporting accuracy matrix may include position variance and / or velocity variance, etc.; this embodiment of the invention does not limit this. Optionally, the electronic device can be converted according to the statistical method corresponding to the reliable position accuracy and velocity accuracy to obtain the position standard deviation and velocity standard deviation (here referring to one standard deviation), thereby determining the position variance and velocity variance; it should be noted that this embodiment of the invention does not limit the specific representation of reliable position accuracy and velocity accuracy, such as the accuracy being one standard deviation or two standard deviations (in which case it needs to be converted to one standard deviation), etc. The current reporting accuracy matrix can also be expressed as the reporting accuracy matrix at the current event time. .

[0079] Optionally, when determining the applicable measurement noise for the current source based on the current reported accuracy matrix, the external reference matrix, and the historical measurement noise of the current source, the electronic device can also determine the reliable evidence quantity of reported accuracy (which may correspond to the reported accuracy evidence of the current track point), the external reference evidence quantity (which may correspond to reference evidence formed by other compatible sources), and the historical evidence quantity of the current source (which may correspond to the historical statistical evidence of the current source). Then, according to the reliable evidence quantity of reported accuracy, the external reference evidence quantity, and the historical evidence quantity of the current source, a matrix-weighted average is performed on the current reported accuracy matrix, the external reference matrix, and the historical measurement noise of the current source to obtain a matrix-weighted average result. The matrix-weighted average result is then constrained based on the measurement noise covariance constraint operator corresponding to the current field pattern to obtain the applicable measurement noise for the current source. Optionally, in other embodiments, the matrix-weighted average result can also be used as the applicable measurement noise for the current source, etc.; this invention does not limit this. The above three evidence quantities can all be equivalent evidence quantities.

[0080] Optionally, if the accuracy of the accompanying report only covers part of the current measurement (e.g., the current measurement includes position and velocity, but only velocity accuracy exists), then the amount of reliable evidence for the accuracy of the accompanying report can be zero. Optionally, in other embodiments, the current field pattern can be further divided into sub-patterns that can be fully covered and processed separately, etc.; the present invention does not limit this.

[0081] Optional, along with the amount of reliable evidence for accuracy. As shown in Formula 2.5: Equation 2.5 in, It can be the maximum amount of equivalent evidence that the accuracy of the report can provide (it can be any value greater than 0, that is, it can be set according to experience or actual needs). It can indicate whether the semantics of the accuracy statistics of the report are clear (also known as the semantic clarity score, such as a value of 1 for clear and a value of 0 for unclear). It can indicate whether a value is valid and within an acceptable range (the value can be [0,1], which can also be called a numerical compliance score, such as a score for precision or variance determined according to a preset numerical scoring rule, which can be set according to experience or actual needs). This indicates whether the current reported accuracy matrix is ​​basically consistent with the current source historical measurement noise or the external reference matrix (the value can be [0,1], where 0 represents complete inconsistency and 1 represents complete consistency; this can also be called a consistency score, such as calculating the Gaussian distance between two covariance matrices and monotonically mapping this distance to the interval [0,1], etc.). Optionally, the embodiments of the present invention do not limit the specific method for determining the credible evidence quantity of reported accuracy; for example, the credible evidence quantity of reported accuracy can also be the product of the maximum equivalent evidence quantity, the semantically explicit score, and the accuracy consistency score, etc.

[0082] Accordingly, the embodiments of the present invention do not limit the specific calculation method of the consistency score; for example, the consistency score can also be as shown in Formula 2.6: Equation 2.6 in, It can be the current source historical measurement noise or the external reference matrix, η R It can be a consistency decay parameter (which can be set according to experience or actual needs, and the value is greater than 0), and ε can be any extremely small positive number.

[0083] Optional, current source historical evidence volume As shown in Formula 2.7: Equation 2.7 Optional, amount of external reference evidence As shown in Formula 2.8: Equation 2.8 in, It can be a pre-configured upper limit for the amount of external reference evidence (which can be set according to experience or actual needs), and can be used to prevent the reference items from increasing indefinitely when there are many sources.

[0084] Accordingly, the applicable measurement noise from the current source As shown in Formula 2.9: Equation 2.9 Optionally, if the denominator in Formula 2.9 is zero, the system conservative prior measurement noise covariance corresponding to the current field pattern can be used, etc. It is evident that embodiments of the present invention can achieve weighted contraction of the equivalent evidence quantity for measurement noise through various evidence quantities. Optionally, in other embodiments, an empirical Bayesian method with the same statistical meaning as the evidence fusion relationship can also be used to determine the applicable measurement noise for the current source. In this case, empirical Bayesian contraction can be used to determine the applicable measurement noise for the current source, etc. Embodiments of the present invention do not limit the specific calculation method for the applicable measurement noise for the current source. It is evident that embodiments of the present invention can use the corresponding equivalent evidence quantity to participate in the contraction of the current source's historical measurement noise and the external reference matrix to determine the applicable measurement noise for the current source. This effectively avoids the current source being weighted repeatedly as both a direct historical item and a reference item, thus preventing the reuse of historical information from the current source through the external reference matrix.

[0085] Based on this, embodiments of the present invention can determine the applicable measurement noise of the current source through statistical contraction. This means that when the current source lacks sufficient valid evidence, has not been updated for a long time, or is in the early stages of recovery, its measurement noise estimation is made closer to the external reference matrix or the prior knowledge of the source category. As the valid evidence of the current source increases, its own historical statistical proportion increases accordingly. For example, when the amount of attenuated valid sample evidence of a new source or new field pattern before the formation of measurement noise in this round is small, the corresponding amount of historical evidence of the current source is small, and the applicable measurement noise of the current source relies more on the external reference matrix or the prior knowledge of the source category. As the source generates more valid track points that pass statistical gating, the amount of updated valid sample evidence accumulates, and the proportion of the current data source's own historical statistics gradually increases in subsequent rounds. For example, the longer a source has not received effective statistical updates, the more the amount of effective sample evidence, the cumulative amount of the second moment of mixed prediction innovation, and the cumulative amount of mixed prediction uncertainty will decrease by the same decay factor when it is accessed again. Since the three decay proportionally, the point estimation structure of the source's historical measurement noise will not suddenly change without basis simply because of the passage of time. However, its equivalent evidence decreases, so when the source is recovered, it will shrink more towards the external reference matrix or the prior of the source category, and so on.

[0086] Optionally, in other embodiments, the applicable measurement noise for the current source may be determined using only the external reference matrix and the historical measurement noise of the current source. For example, the applicable measurement noise for the current source may be determined by matrix weighted averaging of the external reference matrix and the historical measurement noise of the current source; or, the applicable measurement noise for the current source may be determined using only the historical measurement noise of the current source and the current reporting accuracy matrix, etc. The present invention does not limit this.

[0087] S204, Determine the model transition matrix, which includes the model transition probabilities between any two motion models in the set of motion models.

[0088] In this embodiment of the invention, for the motion model m, the discrete state equation can be as shown in Equation 2.10: Equation 2.10 Among them, F m ( t k () can be the motion model m in time interval t k The state transition matrix is ​​obtained by discretizing the kinematic equations of the specific motion model; w m,k This can account for process disturbances caused by unmodeled acceleration, turning variations, and discretization errors in the motion model m; Q m (Δt k The process noise covariance matrix (also known as process noise, etc.) describes the prediction uncertainty of the motion model itself. For example, the position-velocity state block F of a one-dimensional uniform motion model. CV ( t) can be represented as shown in Formula 2.11: Equation 2.11 For example, if unmodeled acceleration is considered as continuous white noise, its corresponding process noise can be formed by the acceleration spectrum intensity and the Δt integral; the uniform acceleration model and the coordinated turning model can respectively construct their corresponding F-values ​​from their equations of motion. m and Q m Optionally, the process noise can be a reference process noise calibrated offline or configured according to the aircraft's motion range (i.e., it can be set according to experience or actual needs); or, the process noise can be determined by adaptive adjustment at subsequent times, etc.; the embodiments of the present invention do not limit this.

[0089] Optionally, the model transition matrix can be set according to experience or actual needs, and this embodiment of the invention does not limit this. Optionally, the motion model in the model transition matrix The model transition probability to motion model m can be expressed as: This indicates that the previous event (i.e., the event indicated by the trackpoint data corresponding to the previous valid event time) adopted a motion model. Under the given conditions, the probability that the current event (i.e., the event indicated by the current waypoint data) will transition to motion model m; M k-1 M can represent the motion model used in the previous event. k It can represent the motion model used for the current event; optionally, the motion model It can also be represented as the first in the set of motion models. There are several motion models. Correspondingly, the model transition probability can be determined by the transition statistics of each motion state in the historical trajectory, or it can be configured according to the expected model dwell time, etc.; that is, the model transition probability from one motion model to another can be set according to experience or actual needs, and this embodiment of the invention does not limit this. Optionally, the model transition matrix can satisfy the constraints shown in Formula 2.12: Equation 2.12 S205, based on the historical model posterior probability and model transition matrix of each motion model in the historical global interactive multi-model state, determine the interaction probability of each motion model. The interaction probability of any motion model includes: the conditional probability of each motion model being used in the previous valid event time under the condition that the current event time uses any motion model.

[0090] Wherein, the current event time adopts motion model m, and the previous valid event time adopts motion model m. The conditional probability can also be expressed as the probability derived from the previous motion model. The interaction probability to the current motion model m. Optional, the motion model... The motion model m can be any motion model in the set of motion models.

[0091] In this embodiment of the invention, a global interactive multi-model state may include, but is not limited to, the posterior motion state, the posterior state estimation error covariance, and the posterior probability of each motion model in the motion model set. The historical posterior probability of a motion model can also be referred to as the posterior probability of the corresponding motion model at the previous valid event time.

[0092] Optionally, for any motion model (such as motion model m) in the set of motion models, the model transition probability from each motion model to any other motion model can be determined from the model transition matrix, and the prediction probability of any other motion model can be calculated using the model transition probability from each motion model to any other motion model and the historical model posterior probability of each motion model; then, the interaction probability of any other motion model can be calculated using the model transition probability from each motion model to any other motion model, the historical model posterior probability of each motion model, and the prediction probability of any other motion model.

[0093] Optional, motion model The model probability (i.e., the posterior probability) at the previous valid event time can be: Optionally, the initial probability of the model can be configured statistically from historical scenarios, i.e., it can be set according to experience or actual needs; this embodiment of the invention does not limit this. For example, when there is no prior preference, a uniform initialization of 1 / M can be used, etc. Optionally, taking any motion model as an example, the predicted probability of motion model m is... As shown in Formula 2.13: Equation 2.13 Accordingly, given that the current event time uses motion model m, the previous valid event time uses motion model m. The conditional probability (i.e., from the previous motion model) (Interaction probability to the current motion model m) As shown in Formula 2.14: Equation 2.14 Based on this, a motion model can be adopted. Model transition probability to motion model m, motion model Based on the historical model posterior probability and the prediction probability of the motion model m, the previous valid event time is calculated using the motion model m, assuming the current event time uses the motion model m. The conditional probability.

[0094] S206, based on the interaction probability of each motion model, determine the mixed initial state and mixed initial covariance of each motion model; and based on the mixed initial state, mixed initial covariance and applicable measurement noise from the current source of each motion model, determine the model innovation and model innovation covariance of each motion model.

[0095] Optionally, when determining the mixed initial state and mixed initial covariance of each motion model based on the interaction probability of each motion model, for any motion model in the motion model set, the mixed initial state of any motion model can be determined based on the interaction probability of any motion model and the posterior motion state of each motion model at the previous valid event time (which can also be called the historical posterior motion state, which can be determined from the historical global interactive multi-model state); and the mixed initial covariance of any motion model can be determined based on the interaction probability of any motion model, the posterior motion state of each motion model at the previous valid event time, and the mixed initial state of any motion model.

[0096] For example, taking any motion model m as an example, the mixed initial state of motion model m is described. As shown in Formula 2.15: Equation 2.15 Correspondingly, the mixed initial covariance of motion model m As shown in Formula 2.16: Equation 2.16 Based on this, the mixed initial covariance of any motion model can be calculated using the interaction probability of any motion model, the posterior motion state of each motion model at the previous valid event time, the posterior state estimation error covariance of each motion model at the previous valid event time, and the mixed initial state of any motion model.

[0097] Optionally, when determining the model innovation and model innovation covariance of each motion model based on the mixed initial state, mixed initial covariance, and applicable measurement noise from the current source, for any motion model in the set of motion models, model prediction can be performed according to the mixed initial state and mixed initial covariance of any motion model to obtain the prior prediction state and prior prediction covariance of any motion model at the current event time; and the model innovation and model innovation covariance of any motion model can be determined based on the prior prediction state, prior prediction covariance, and applicable measurement noise from the current source of any motion model at the current event time.

[0098] For example, taking any motion model m as an example, the prior predicted state of motion model m at the current event time is explained. and prior prediction covariance As shown in Formula 2.17: Equation 2.17 Here, k|k-1 can represent a priori prediction of the current event state based on information up to the previous event.

[0099] Optionally, when determining the model innovation and model innovation covariance of any motion model based on its prior prediction state, prior prediction covariance, and applicable measurement noise at the current event time, the prior prediction state of any motion model at the current event time can be used to determine the predicted measurement of any motion model. For example, the predicted measurement of motion model m... As shown in Formula 2.18: Equation 2.18 Then, the model innovation of any motion model can be calculated using the current measurement vector and the predicted measurement of any motion model; where innovation can refer to the difference between the current measurement and the prior predicted measurement (i.e., the predicted measurement). For example, the model innovation of motion model m... As shown in Formula 2.19: Equation 2.19 Furthermore, the model innovation covariance of any motion model can be calculated based on the applicable measurement noise from the current source and the prior prediction covariance of any motion model at the current event time. For example, the model innovation covariance of motion model m... As shown in Formula 2.20: Equation 2.20 S207 determines the posterior motion state, posterior state estimation error covariance, and model posterior probability of each motion model at the current event time based on the model innovation and model innovation covariance of each motion model, so as to determine the current global interactive multi-model state of the target aircraft at the current event time.

[0100] The current global interactive multi-model state can include the posterior motion state of each motion model at the current event time, the posterior state estimation error covariance, and the model posterior probability.

[0101] Optionally, for the m-th motion model in the set of motion models, the electronic device can determine the Kalman gain of the m-th motion model based on the model innovation covariance; and determine the posterior motion state of the m-th motion model at the current event time based on the Kalman gain and model innovation of the m-th motion model. Then, the posterior state estimation error covariance of the m-th motion model at the current event time can be determined based on the Kalman gain of the m-th motion model and the applicable measurement noise from the current source. Correspondingly, the model likelihood of the m-th motion model can be determined based on the model innovation covariance and model innovation of the m-th motion model; and the posterior probability of the m-th motion model can be determined based on the model likelihood of the m-th motion model.

[0102] For example, the Kalman gain of motion model m As shown in Formula 2.21: Equation 2.21 For example, the posterior motion state of motion model m at the current event time can be represented by Equation 2.22: Equation 2.22 For example, the posterior state estimation error covariance (also known as the posterior covariance) of the motion model m at the current event time can be expressed as shown in Equation 2.23: Equation 2.23 Furthermore, the model likelihood (also known as the measured likelihood density) of the motion model m. As shown in Formula 2.24: Equation 2.24 Based on this, the model likelihood of motion model m can be the measurement probability density value obtained from the prior predicted state of motion model m, the model innovation covariance, and the current measurement conditions, and can be used to compare the explanatory power of each motion model for the current waypoint.

[0103] Correspondingly, the posterior probability of the motion model m can be expressed as shown in Equation 2.25: Equation 2.25 Based on this, the same current source of applicable measurement noise can simultaneously enter S. m,k K m,k posterior covariance and Λ m,k When the current source error is large, the large current source applicable measurement noise will increase the model information covariance and correspondingly reduce the excessive influence of the measurement on the state and model likelihood, rather than directly absorbing the source error by increasing the model process noise or re-initializing the motion model.

[0104] Optionally, embodiments of the present invention may employ the aforementioned interactive multi-model Kalman state estimation framework to estimate the global interactive multi-model state. For example, for linear state transitions and linear measurement relationships, each motion model may employ standard Kalman filtering; for nonlinear measurements such as ground speed, extended Kalman filtering or unscented Kalman filtering may be employed. Alternatively, in other embodiments, square root filtering may be used, such as inputting the prior predicted state, the square root of the prior predicted covariance, the current measurement, the measurement model, and the square root of the applicable measurement noise from the current source, to output the posterior motion state of the model and the square root of the posterior state estimation error covariance, etc. The present invention does not limit this.

[0105] S208, based on the current global interactive multi-model state, determines the current global state of the target aircraft at the current event time; the current global state supports the updating of the global estimated track of the target aircraft.

[0106] Optionally, the electronic device can use the posterior motion states and posterior probabilities of each motion model at the current event time to calculate the current global state of the target aircraft at the current event time (which can also be represented as the global state of the target aircraft at the current event time). For example, the current global state... As shown in Formula 2.26: Equation 2.26 Optionally, the electronic device can also use the current global interactive multi-model state to calculate the current global covariance of the target aircraft at the current event time (which can also be expressed as the global covariance of the target aircraft at the current event time). Based on this, the electronic device can determine the global posterior merging result of the target aircraft at the current event time based on the current global interactive multi-model state. The global posterior merging result of the target aircraft at the current event time may include the current global state and the current global covariance. For example, the current global covariance... As shown in Formula 2.27: Equation 2.27 Optionally, the electronic device may use the current global state and / or the current global covariance to generate a new fused waypoint (also referred to as the current fused waypoint or waypoint data used to indicate the new fused waypoint). For example, the new fused waypoint may include, but is not limited to, at least one of the following: the current event time, the current global state, and the current global covariance, etc., and this embodiment of the invention does not limit this. Accordingly, the new fused waypoint can be added to the global estimated waypoint to update the global estimated waypoint.

[0107] S209, Based on the applicable source measurement noise, determine the sufficient statistical state of the source measurement noise of the target aircraft at the current event time, so as to use it for the state estimation of the target aircraft at the next valid event time.

[0108] Optionally, the electronic device may determine the hybrid prediction measurement based on the prediction measurements of each motion model; and use the difference between the current measurement vector in the current waypoint data and the hybrid prediction measurement as the hybrid prediction information; and determine the hybrid prediction uncertainty based on the hybrid prediction measurement and the prediction measurements of each motion model.

[0109] For example, hybrid predictive measurement As shown in Formula 2.28: Equation 2.28 Based on this, this round of source statistics updates incorporates model prediction probabilities from before the current measurement. For example, hybrid prediction of new information... As shown in Formula 2.29: Equation 2.29 Optionally, the mixed prediction uncertainty can be a mixed prediction uncertainty that does not include noise from current source measurements. An example is the mixed prediction uncertainty. As shown in Formula 2.30: Equation 2.30 The first term within the square brackets can be the covariance of the state prediction error (i.e., the prior prediction covariance) of the motion model m mapped to the current measurement space. z m,k This can be the deviation of the predicted measurement of motion model m relative to the mixed predicted measurement. It is evident that, in this embodiment of the invention, the model prediction probability before the measurement update can be used to form the mixed predicted measurement during source statistical updates. The mixed predicted uncertainty simultaneously includes the internal state uncertainty of each model (i.e., the first term within square brackets) and the dispersion of the predicted mean between models (i.e., the second term within square brackets). This more accurately distinguishes between motion model uncertainty and source measurement uncertainty, reducing the risk of inter-model prediction differences being incorrectly included in measurement noise. This effectively solves the statistical bias problem caused by simply averaging model innovation or only deducting the intra-model covariance.

[0110] In this embodiment of the invention, let F k-1 This indicates that all historical information acquired before absorbing the current waypoint (such as previously received waypoints, global interactive multi-model states, and fully statistically significant source measurement noise states, etc.) is available; the motion model set can approximately describe the target motion, the model predictions are statistically consistent, and the current source measurement error is... Under the nominal condition that the error vector of random measurement (i.e., the current source is real but cannot be directly observed) and the state prediction error are independent, the following relationship can exist as shown in Equation 2.31: Equation 2.31 Therefore, it can be shown in Formula 2.32: Equation 2.32 in, This can represent the approximate true measurement noise covariance matrix (also called the true measurement noise covariance matrix) of the random measurement error vector. Based on this, embodiments of the present invention can accumulate the mixed prediction innovation and mixed prediction uncertainty into A (i.e., the accumulated second moment of the mixed prediction innovation) and B (i.e., the accumulated amount of mixed prediction uncertainty), respectively, and then approximate the above expectation through long-term sample averaging, thereby obtaining the approximate true measurement noise covariance matrix; that is, accumulating the second moment of the mixed prediction innovation for the same source and the same field pattern, and subtracting the corresponding accumulated amount of mixed prediction uncertainty, the long-term average can be used to obtain the approximate true measurement noise covariance matrix under nominal conditions. Therefore, by updating A, B, and N (i.e., the effective sample evidence), (AB) / N can be used as the basis for online measurement noise statistics, which can then be used to estimate the true measurement noise covariance matrix. It can be seen that embodiments of the present invention provide the basis for the second moment of source measurement noise statistics, i.e., the second moment of the mixed prediction innovation, through formulas 2.31 and 2.32. The expectation can be equal to "mixed prediction uncertainty + measurement noise". Therefore, the long-term cumulative mixed prediction innovation second moment A, after deducting the cumulative mixed prediction uncertainty B, and dividing by the effective sample evidence N, can estimate the true source measurement noise covariance. This is the theory of fully statistical state preservation of A, B, and N.

[0111] Furthermore, the electronic device can determine the statistical weights at the current event time based on the applicable measurement noise from the current source, the mixed prediction uncertainty, and the mixed prediction innovation; and based on the statistical weights at the current event time, determine the sufficient statistical state of the source measurement noise of the target aircraft at the current data source, the current field mode, and the current event time (that is, obtain the sufficient statistical state of the source measurement noise of the target aircraft at the current data source, the current field mode, and the kth valid event time). The sufficient statistical state of the source measurement noise of the target aircraft at the current event time includes the sufficient statistical state of the source measurement noise of the target aircraft at the current data source, the current field mode, and the current event time, and thus obtain the sufficient statistical state of the source measurement noise of the target aircraft at the kth valid event time.

[0112] Optionally, when determining the statistical weights at the current event time based on the applicable measurement noise from the current source, the mixed prediction uncertainty, and the mixed prediction innovation, the normalized mixed innovation square can be calculated using the applicable measurement noise from the current source, the mixed prediction innovation, and the mixed prediction uncertainty; and the statistical weights at the current event time can be determined based on the normalized mixed innovation square. For example, the normalized mixed innovation square ξ k As shown in Formula 2.33: Equation 2.33 Optionally, embodiments of the present invention may define a robust gating function g(ξ). k (Also known as statistical gating strategy or robust statistical gating, etc.), as shown in Equation 2.34: Equation 2.34 Optionally, τ1 and τ2 can be set according to experience or actual needs, or they can be based on d. k The chi-square distribution of the degrees of freedom can be selected, and the abnormal false alarm rate and false negative rate of the actual flight data can also be used for calibration, etc.; the embodiments of the present invention do not limit this; for example, the initial value can be given by the chi-square quantile, such as configuring τ1 as a higher quantile (such as 95% quantile) and τ2 as an even higher quantile (such as 99.9% quantile), and then calibrating according to the abnormal false alarm rate and false negative rate of the actual flight data, etc.

[0113] Based on this, the statistical weight ω at the current event time k As shown in Formula 2.35: Equation 2.35 Optional, ω k It can be used solely to control the update intensity of the source measurement noise in a sufficiently statistical state. It should be noted that the specific calculation method of statistical weights is not limited in the embodiments of the present invention; for example, Huber-type weights, Tukey-type weights, heavy-tailed probability models, or Gaussian mixture anomaly models can also be used to reduce the impact of outlier waypoints on long-term statistics, thereby replacing the calculation of statistical weights through robust gating functions, etc.

[0114] Optionally, whether a trackpoint participates in the current round of fully statistical state update of source measurement noise can be determined by an independent state update gating strategy; optionally, the state update gating strategy can be set according to experience or actual needs, and this embodiment of the invention does not limit this. For example, slightly anomalous trackpoints can participate in restricted state updates by increasing the applicable measurement noise of the current source or limiting the Kalman gain, while strongly anomalous trackpoints (such as trackpoints whose normalized mixed innovation square is greater than the anomalous rejection threshold) can directly skip measurement updates; correspondingly, when ω k When =0, the current track point is not written into the source measurement noise sufficient statistical state. Based on this, embodiments of the present invention can determine whether and to what extent the current track point participates in the Kalman state update through a state update gating strategy, and determine whether to write the current track point into A, B, N, etc. through a statistical gating strategy.

[0115] Optionally, let the state threshold be τ. state,1 <τ state,2 , τ state,2 This can be an anomaly rejection threshold, when ξ k ≤τ state,1 Normal measurement updates occur when τ state,1 <ξ k ≤τ state,2 When ξ is determined to be a slight anomaly, the Kalman gain can be indirectly reduced by amplifying the applicable measurement noise from the current source used in this round of state updates; when ξ k >τ state,2 When a strong physical boundary is violated, it is determined to be a strong anomaly, and measurement updates can be skipped. In this embodiment of the invention, whether the statistical state is written to the current trackpoint is still determined by g(ξ). k ) and ω k Decision. Optional, τ state,1 and τ state,2 These settings can be based on experience or actual needs, or they can be obtained through calibration, etc.; this embodiment of the invention does not limit this. Optionally, in other embodiments, ω can also be set when the current waypoint is determined to be slightly abnormal and / or strongly abnormal. k Setting it to 0, etc.; this invention does not limit this.

[0116] For example, in the case of a slight anomaly, the applicable measurement noise of the current source can be updated according to formula 2.36 to obtain the updated applicable measurement noise of the current source. : Equation 2.36 Optional, β k The settings can be based on experience or actual needs, and this embodiment of the invention does not limit this. Based on this, the electronic device can recalculate the model information covariance and Kalman gain of each motion model in the current round based on the updated applicable measurement noise from the current source, thereby determining the current global interactive multi-model state to achieve constrained state updates, and so on.

[0117] Optionally, when determining the sufficiently statistical state of the source measurement noise of the target aircraft at the current event time based on the statistical weights at the current event time, the current parameter values ​​of each model measurement parameter (i.e., the model measurement parameters of the target aircraft at the previous data source, current field mode, and current event time (here, the kth effective event time)) can be determined based on the statistical weights at the current event time and the decay model measurement parameters of each model measurement parameter at the current data source and current field mode (i.e., the decay results of the model measurement parameters at the current data source, current field mode, and the previous effective event time, which can also be called the decay model measurement parameters at the current data source, current field mode, and current event time). For example, the current effective sample evidence, current mixed prediction innovation second moment accumulation, and current mixed prediction uncertainty accumulation of the target aircraft at the current data source, current field mode, and current event time can be determined based on the statistical weights at the current event time, the decay effective sample evidence, the decay mixed prediction innovation second moment accumulation, and the decay mixed prediction uncertainty accumulation of the target aircraft at the current data source, current field mode, and current event time. For example, the current effective sample evidence quantity, the current cumulative second moment of mixed prediction innovation, and the current cumulative mixed prediction uncertainty can be represented as Equation 2.37: Equation 2.37 Optionally, when ω k When the value is greater than 0, the current event time can also be used as the most recent valid update event time of the target aircraft under the current data source, current field mode, and current event time (which is the kth valid event time), as shown in Formula 2.38: Equation 2.38 Based on this, the electronic device can determine the sufficient statistical state of the source measurement noise of the target aircraft under the current data source, current field mode, and current event time (which can be the kth valid event time). The sufficient statistical state of the source measurement noise of the target aircraft under the current data source, current field mode, and current event time may include, but is not limited to, at least one of the following: the amount of valid sample evidence of the target aircraft under the current data source, current field mode, and current event time (i.e., the current amount of valid sample evidence), the cumulative amount of the second moment of the mixed prediction innovation (i.e., the current cumulative amount of the second moment of the mixed prediction innovation), the cumulative amount of the mixed prediction uncertainty (i.e., the current cumulative amount of the mixed prediction uncertainty), the time of the most recent valid update event, statistical confidence, source quality score, and source health score, etc.

[0118] In this embodiment of the invention, the updated A, B, and N do not recalculate the applicable source measurement noise for the current round, but are instead saved to the cross-round stable state and take effect when processing the next and subsequent waypoints. Based on this, after completing the state estimation for the k-th valid event time (e.g., after obtaining the global state, the fully statistical state of the source measurement noise, etc., for the k-th valid event time), k can be incremented by 1 to continue the state estimation for the next valid event time. In other words, the fully statistical state of the source measurement noise of the target aircraft at the current event time can be used for future events.

[0119] Optionally, under the nominal conditions that the random measurement error vector satisfies zero mean, is independent of the state prediction error condition, the motion model set can approximately describe the target motion, the prediction probability and prediction covariance have statistical consistency, and no data dependency anomaly gating is used, the statistical estimation results of the measurement noise covariance at data source i, field pattern g, and the kth valid event time are provided. As shown in Formula 2.39: Equation 2.39 Where, ε N The effective sample evidence quantity can be set according to experience or actual needs, and this embodiment of the invention does not limit this. Based on this, as the effective sample evidence quantity increases, the statistical estimation result of the measurement noise covariance can approach the true measurement noise covariance under data source i and field pattern g. .

[0120] Based on this, embodiments of the present invention can introduce robust statistical gating, state update gating, upper and lower bounds of eigenvalues, statistical confidence, and source health scores to achieve sample selection and weighting, thereby effectively reducing the contamination of online measurement noise (i.e., the true measurement noise covariance) statistics by deviation points, abnormal sources, model mismatch, and limited samples. In other words, embodiments of the present invention can effectively improve the accuracy of the sufficient statistical state of source measurement noise, and further improve the accuracy of the measurement noise covariance statistical estimation results through the sufficient statistical state of source measurement noise with higher accuracy, so as to achieve accurate estimation of the true measurement noise covariance, and so on.

[0121] Optionally, for data source i, when at least one piece of anomalous evidence appears consecutively, adjustments can be made according to anomaly control strategies. Optionally, the anomaly control strategies can be set according to experience or actual needs, and the embodiments of the present invention do not limit this. For example, the anomaly control strategies can be to sequentially execute the following: reduce the source quality score, reduce the source health score, freeze statistical updates (such as not writing the current round of track points into A, B, N), suspend measurement status updates (i.e., suspend global interactive multi-model status updates), and isolate the source, etc. Optionally, at least one piece of anomalous evidence may include, but is not limited to, at least one of the following: the normalized mixed innovation square exceeds the anomalous rejection threshold (or exceeds the anomalous rejection threshold E times consecutively, where E is a positive integer); the trackpoint position, velocity, acceleration, or time difference violates the configured physical boundaries; the event time continuously regresses, the timestamp is non-compliant, or the transmission delay is significantly abnormal; the statistical semantics of the reported accuracy are unreliable or the numerical values ​​are non-compliant for a long period of time; when generating source historical measurement noise, the unconstrained original matrix triggers significant eigenvalues ​​(such as significantly large eigenvalues) or channel upper and lower bound constraints multiple times consecutively; the source has a continuous systematic deviation in the same direction relative to the global prediction state, etc.; the embodiments of the present invention do not limit this. Optionally, the mean of the prediction residuals of the source can be recursively estimated to determine the continuous systematic deviation, such as the result of the recursive estimation of the mean of the prediction residuals. As shown in Formula 2.40: Equation 2.40 Where, η b The smoothing coefficient can be a value belonging to (0,1); optionally, the smoothing coefficient can be set according to experience or actual needs, or it can be determined by calibration, and the embodiments of the present invention do not limit this; for example, it can be based on the expected effective memory length L. b Set to 2 / (L) b +1), or it can be determined by historical data, etc.; optionally, the expected effective memory length can be set according to experience or actual needs, and the embodiments of the present invention do not limit this. Accordingly, if in continuous L bIf, among the valid events from this source, the recursive estimation results of the mean of the predicted residuals maintain a consistent direction, and their normalized bias energy (calculated by performing normalized magnitude calculation to measure how much the estimation bias is relative to the uncertainty) consistently exceeds the bias threshold, then it is determined that this source has a systematic bias relative to the global prediction. For example, the normalized bias energy ζ of data source i... i,k As shown in Formula 2.41: Equation 2.41 in, It can be used to estimate the uncertainty of the mean residual within the same window (e.g., it can be used to estimate the covariance of the recursive estimate of the mean residual), ε b The value is a positive 0 regularization term (which can be set according to experience or actual needs), and I represents the identity matrix. Optionally, the deviation threshold can be set according to experience or actual needs, or it can be calibrated according to normal / abnormal samples or the expected false alarm rate. This embodiment of the invention does not limit this.

[0122] Optionally, when a source that has been isolated or downgraded reappears with a trackpoint that meets the basic format, time, and physical validity requirements, it can enter a "recovery in progress" state instead of immediately returning to a normal state. Optionally, a recovery determination can be performed on sources in the recovery in progress state, thereby restoring sources that meet the recovery conditions to a normal state. It should be noted that the specific content of the recovery conditions is not limited in the embodiments of the present invention. For example, the recovery conditions may include, but are not limited to, at least one of the following: continuous L rec The source trackpoint meets basic quality gating requirements, such as field integrity, time validity, physical boundaries, and reporting status; the normalized mixed information square does not exceed the recovery statistics threshold; the source quality score is not lower than the configured minimum recovery value; and evidence of the aforementioned systematic bias no longer persists, etc. Optional, L rec The recovery statistical threshold and recovery minimum value can be set based on experience or actual needs, or calibrated based on statistics of normal and abnormal samples or the target false alarm rate. This embodiment of the invention does not limit these settings. Optionally, in the initial recovery phase, the source health status can be set to "recovering," and the source health score can be set to a limit value lower than the normal state. As the number of consecutive valid events increases, the source health score can be gradually increased using a step or continuous function until the normal state conditions are met, at which point it recovers to the normal range. Optionally, the global interactive multi-model state does not need to be reinitialized during the recovery process.

[0123] Optionally, when no valid waypoints arrive during a certain period, no new statistical evidence is added for A, B, and N from any source, nor is the motion prediction state maintained separately for each source. In this case, only a global interactive multi-model state can be propagated. That is, only the global interactive multi-model state can be used to perform "model probability propagation, necessary model interaction, and time prediction of each motion model"; or, after the current waypoint is processed, the posterior motion state of each motion model can be propagated to the future separately, and then merged according to the future model probability to achieve effective time-domain prediction, etc. Optionally, the electronic device can perform finite time-domain prediction according to the prediction step size. The prediction step size can be set according to experience or actual needs, and this embodiment of the invention does not limit it. For example, the prediction result of motion model m at prediction time h (i.e., the predicted motion state) and predicted covariance It can be shown in Equation 2.42: Equation 2.42 in, This can be the cumulative model process noise covariance from the current time to the prediction time (i.e., model process noise, which can refer to the uncertainty of state evolution corresponding to the motion model, and can be used to express the mismatch of motion models such as unmodeled acceleration, turning, climbing and descending). The longer the prediction time, the larger the model process noise covariance is usually; F m (h) can represent the state transition matrix propagating from the current time to the prediction time. Correspondingly, the model probability can be propagated according to the model transition matrix, forming the future global prediction state, global prediction covariance, and prediction model probability. Optionally, h can be any prediction time determined according to the prediction step size. Each prediction step can sequentially execute model probability propagation, necessary model interactions, and prediction of the state without measurement. Based on this, embodiments of the present invention can output the prediction results of each motion model at each prediction time within the prediction period, and / or the global prediction state, global prediction covariance, and prediction model probability at each prediction time, etc. Optionally, the prediction period can be determined based on a preset estimated duration, such as the prediction period being a preset estimated duration, and the start time of the prediction period being the current time (e.g., the current event time, etc.). Optionally, the preset estimated duration can be set according to experience or actual needs; embodiments of the present invention do not limit this. Optionally, after exceeding the configured maximum no-observation time limit, global prediction covariance threshold, or model process noise covariance range, a degraded state or target loss state can be output, and the high-confidence fusion state is not continued to be output, etc.

[0124] Correspondingly, the model probability can also be propagated step by step according to the model transition relationship; for example, for the r-th prediction time in the future (the r-th prediction time can be specifically represented as h) r ), motion model m at prediction time h rThe predicted model probability can be shown in Equation 2.43: Equation 2.43 Here, Π can represent the model transition matrix. Based on this, the future global prediction state and global prediction covariance can be merged according to the corresponding prediction model probability at each prediction time. For example, the global prediction state and global prediction covariance at any prediction time can be determined according to the prediction model probability, prediction motion state and prediction covariance of each motion model at any prediction time.

[0125] Optionally, if the current data source is healthy, the current waypoint has not been abnormally rejected, and evidence of model mismatch persists, the electronic device can also adjust the model process noise at subsequent time points. Optionally, the normalized mismatch statistics of model m (also known as normalized innovation energy) can be adjusted. J m,k As shown in Formula 2.44: Equation 2.44 In the case of statistical matching, the long-term magnitude of the normalized innovation energy is close to 1. Correspondingly, evidence of persistent mismatch... As shown in Formula 2.45: Equation 2.45 in, η J It can be an exponential smoothing coefficient belonging to (0,1], used to determine the memory length of mismatch evidence; τ J The threshold is set as the baseline threshold for a single model mismatch. Optionally, both can be calibrated by the application scenario, such as being determined based on the expected memory window length and the information statistics of normal flight data, or they can be set according to experience or actual needs. This embodiment of the invention does not limit this. Based on this, when the evidence of persistent mismatch exceeds the persistent mismatch threshold (which can be set according to experience or actual needs), or when two or more different health sources successively provide consistent maneuver evidence within the set event window, the model process noise used in the next event is allowed to increase. The so-called "consistent maneuver evidence" means that the trackpoints arriving independently from different sources all support the same sign or the same direction of acceleration, turning or climbing trends, rather than only the simultaneous increase in information amplitude. Optionally, for the motion model m, the next event process noise scale α m,k+1 As shown in Formula 2.46: Equation 2.46 Among them, C Q The mapping coefficient α can represent the mapping from mismatch evidence to the process noise amplification factor. min and α maxThe upper and lower bounds of the scale can be represented; optionally, the mapping coefficients and the upper and lower bounds of the scale can be set according to experience or actual needs, and the embodiments of the present invention do not limit this. Based on this, the model process noise Q used in the next event m,k+1 As shown in Formula 2.47: Equation 2.47 in, This can be the reference process noise covariance of the motion model m; optionally, the reference process noise covariance can be determined based on the continuous white noise spectral density matrix corresponding to the motion model, such as based on the state transition matrix of the motion model for the continuous white noise in the time interval [0, ... t k+1 The reference process noise covariance is obtained by performing analytical integration, or it can be set according to experience or actual needs; this embodiment of the invention does not limit this. Accordingly, The operator can be a legality constraint operator for the process noise matrix, and can be set according to experience or actual needs. This embodiment of the invention does not limit this.

[0126] As can be seen, the embodiments of the present invention can maintain a sufficiently statistical state of measurement noise (i.e., a sufficiently statistical state of source measurement noise) across processing rounds for each data source. Based on event-time decay statistical evidence, it uses an external reference matrix excluding the current source and statistical contraction of equivalent evidence quantity to determine the applicable measurement noise for the current source. This applicable measurement noise drives a continuous global interactive multi-model state estimator, and the sufficiently statistical state of source measurement noise for the current source is updated with a delay after the global posterior is formed. This solves the problems of source switching state jumps caused by fixed or globally uniform measurement noise, mutual contamination of error statistics from different sources, and misjudgment of source changes as aircraft maneuvers. Furthermore, the embodiments of the present invention can limit the immediate reverse influence of a single abnormal waypoint on its own updated weights, thereby eliminating the same-round closed loop where the current waypoint first changes its own measurement noise and then its own Kalman gain, etc.

[0127] This invention acquires the current waypoint data of a target aircraft. When the current waypoint data satisfies the global state candidate update conditions of the target aircraft, it determines the historical global interactive multi-model state of the target aircraft at the previous valid event time, and also determines the historical current source measurement noise fully statistical state of the target aircraft at the previous valid event time, the current data source, and the current field mode. Then, based on the current waypoint data and the historical current source measurement noise fully statistical state, the historical measurement noise of the current source can be determined; and based on the historical measurement noise of the current source, the applicable measurement noise of the current source can be determined. Furthermore, the model transition matrix can be determined, and the interaction probability of each motion model can be determined based on the historical model posterior probability and the model transition matrix of each motion model in the historical global interactive multi-model state. Accordingly, the mixed initial state and mixed initial covariance of each motion model can be determined based on the interaction probability of each motion model. Furthermore, the model innovation and model innovation covariance of each motion model can be determined based on the mixed initial state, mixed initial covariance, and applicable measurement noise from the current source. Thus, based on the model innovation and model innovation covariance of each motion model, the posterior motion state, posterior state estimation error covariance, and model posterior probability of each motion model at the current event time can be determined, thereby enabling the determination of the current global interactive multi-model state of the target aircraft at the current event time. Based on this, the current global state of the target aircraft at the current event time can be determined based on the current global interactive multi-model state; and the source measurement noise fully statistical state of the target aircraft at the current event time can be determined based on the applicable measurement noise from the current source. As can be seen, the embodiments of the present invention can effectively reduce state jumps and state update intensity mismatches caused by source switching. In other words, by dynamically forming the applicable measurement noise of the current source, sources with different error levels can obtain state update intensity that matches their current statistical evidence (such as the three types of equivalent evidence quantities that form the applicable measurement noise of the current source), thereby reducing state jumps or insufficient corrections after source switching, and thus effectively improving the accuracy of aircraft state estimation.

[0128] Based on the description of the relevant embodiments of the above-mentioned aircraft state estimation method, this invention also proposes an aircraft state estimation device, which can be a computer program (including program code) running in an electronic device; such as Figure 3 As shown, the aircraft state estimation device may include an acquisition unit 301 and a processing unit 302. The aircraft state estimation device can perform... Figure 1 or Figure 2 The aircraft state estimation method shown, i.e., the aircraft state estimation device, can operate the above-described unit: The acquisition unit 301 is used to acquire the current waypoint data of the target aircraft. The current waypoint data includes the current event time, the current field mode, and the current source identifier, which is used to indicate the current data source. Processing unit 302 is configured to, when detecting that the current waypoint data satisfies the global state candidate update condition of the target aircraft, determine the historical global interactive multi-model state of the target aircraft at the previous valid event time, and determine the historical current source measurement noise sufficient statistical state of the target aircraft at the previous valid event time, the current data source, and the current field mode; wherein, an event time in the waypoint data used to update the global state of the target aircraft is regarded as a valid event time of the target aircraft; The processing unit 302 is further configured to determine the historical measurement noise of the current source based on the current track point data and the sufficient statistical state of the historical current source measurement noise; and to determine the applicable measurement noise of the current source based on the historical measurement noise of the current source. The processing unit 302 is further configured to determine the current global interactive multi-model state of the target aircraft at the current event time based on the current source applicable measurement noise and the historical global interactive multi-model state; and to determine the current global state of the target aircraft at the current event time based on the current global interactive multi-model state; the current global state supports updating the global estimated trajectory of the target aircraft; The processing unit 302 is further configured to determine the source measurement noise sufficient statistical state of the target aircraft at the current event time based on the current source applicable measurement noise, so as to use the state estimation of the target aircraft at the next effective event time.

[0129] In one implementation, a sufficient statistical state of source measurement noise includes at least one model metric parameter and the time of the most recent valid update event; when determining the historical measurement noise of the current source based on the current waypoint data and the historical sufficient statistical state of the current source measurement noise, the processing unit 302 may specifically be used for: From the fully statistical state of the historical current source measurement noise, determine the time of the most recent valid update event of the current source; and based on the difference between the current event time and the most recent valid update event time of the current source, determine the current source attenuation factor; According to the current source attenuation factor, the model metric parameter in the fully statistical state of the historical current source measurement noise is attenuated to obtain at least one attenuated model metric parameter. Determine the prior measurement noise covariance of the current data source and the current field mode, and determine the historical measurement noise of the current source based on the prior measurement noise covariance and the at least one attenuation model metric parameter.

[0130] In another embodiment, when the processing unit 302 determines the applicable measurement noise for the current source based on the historical measurement noise of the current source, it may specifically be used to: Determine the set of compatible source statistical metadata corresponding to the current statistical metadata consisting of the current data source and the current field pattern. The data source in a set of compatible source statistical metadata is a data source other than the current data source in the source set. Based on the sufficient statistical state of the source measurement noise of the target aircraft under each compatible source statistical metadata in the previous valid event time and the compatible source statistical metadata set, the source historical measurement noise of the target aircraft under the current event time and each compatible source statistical metadata is determined. Based on the target aircraft's historical measurement noise at the current event time and under the statistical metadata of each compatible source, a source external reference matrix is ​​determined; and based on the source external reference matrix and the current source historical measurement noise, the applicable measurement noise for the current source is determined.

[0131] In another embodiment, the current waypoint data further includes an external measurement uncertainty parameter; when the processing unit 302 determines the applicable measurement noise for the current source based on the external reference matrix and the historical measurement noise of the current source, it can specifically be used for: Based on the external measurement uncertainty parameters, determine the current reporting accuracy matrix; Based on the current reported accuracy matrix, the external reference matrix of the source, and the historical measurement noise of the current source, the applicable measurement noise of the current source is determined.

[0132] In another implementation, a global interactive multi-model state includes the posterior motion state of each motion model in the motion model set, the posterior state estimation error covariance, and the model posterior probability; when the processing unit 302 determines the current global interactive multi-model state of the target aircraft at the current event time based on the current source applicable measurement noise and the historical global interactive multi-model states, it can specifically be used to: Determine the model transition matrix, which includes the model transition probabilities between any two motion models in the set of motion models; Based on the historical model posterior probability of each motion model in the historical global interactive multi-model state and the model transition matrix, the interaction probability of each motion model is determined. The interaction probability of any motion model includes the conditional probability of the previous valid event time using each motion model under the condition that the current event time uses any of the motion models. Based on the interaction probabilities of each motion model, the mixed initial state and mixed initial covariance of each motion model are determined respectively; and based on the mixed initial state, mixed initial covariance of each motion model and the applicable measurement noise from the current source, the model innovation and model innovation covariance of each motion model are determined respectively. Based on the model innovation and model innovation covariance of each motion model, the posterior motion state, posterior state estimation error covariance, and model posterior probability of each motion model at the current event time are determined, so as to determine the current global interactive multi-model state of the target aircraft at the current event time.

[0133] In another implementation, when the processing unit 302 determines the posterior motion state, posterior state estimation error covariance, and model posterior probability of each motion model at the current event time based on the model innovation and model innovation covariance of each motion model, it may specifically be used to: For the m-th motion model in the set of motion models, the Kalman gain of the m-th motion model is determined based on the model innovation covariance of the m-th motion model; and the posterior motion state of the m-th motion model at the current event time is determined based on the Kalman gain and model innovation of the m-th motion model. Based on the Kalman gain of the m-th motion model and the applicable measurement noise from the current source, determine the posterior state estimation error covariance of the m-th motion model at the current event time; Based on the model innovation covariance and model innovation of the m-th motion model, the model likelihood of the m-th motion model is determined; and based on the model likelihood of the m-th motion model, the posterior probability of the m-th motion model is determined.

[0134] In another embodiment, when the processing unit 302 determines a sufficiently statistical state of the source measurement noise of the target aircraft at the current event time based on the current applicable source measurement noise, it may specifically be used to: Based on the predicted measurements of each motion model, a hybrid predicted measurement is determined; The difference between the current measurement vector in the current waypoint data and the hybrid prediction measurement is used as the hybrid prediction information; and the hybrid prediction uncertainty is determined based on the hybrid prediction measurement and the prediction measurement of each motion model. Based on the applicable measurement noise from the current source, the mixed prediction uncertainty, and the mixed prediction innovation, determine the statistical weight at the current event time; Based on the statistical weights at the current event time, the source measurement noise sufficient statistical state of the target aircraft at the current data source, the current field mode, and the current event time is determined. The source measurement noise sufficient statistical state of the target aircraft at the current event time includes the source measurement noise sufficient statistical state of the target aircraft at the current data source, the current field mode, and the current event time.

[0135] According to one embodiment of the present invention, Figure 3 Each unit in the aircraft state estimation device shown can be individually or entirely merged into one or more other units, or one or more of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effect of the embodiments of the present invention. The above units are based on logical function division. In practical applications, the function of one unit can also be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of the present invention, any aircraft state estimation device may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0136] According to another embodiment of the present invention, it is possible to perform operations such as those described above by running on a general-purpose electronic device, such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). Figure 1 or Figure 2 The computer program (including program code) involved in each step of the corresponding method shown, to construct such... Figure 3 The diagram illustrates an aircraft state estimation device and a method for implementing embodiments of the present invention. The computer program may be stored on, for example, a computer storage medium, loaded onto the aforementioned electronic device via the computer storage medium, and run therein.

[0137] Based on the description of the method and apparatus embodiments above, an exemplary embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method according to an embodiment of the present invention.

[0138] An exemplary embodiment of the present invention also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0139] An exemplary embodiment of the present invention also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a method according to an embodiment of the present invention.

[0140] refer to Figure 4 The present invention will now be described in the form of a structural block diagram of an electronic device 400 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0141] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0142] Multiple components in electronic device 400 are connected to I / O interface 405, including: input unit 406, output unit 407, storage unit 408, and communication unit 409. Input unit 406 can be any type of device capable of inputting information to electronic device 400. Input unit 406 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 407 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 408 may include, but is not limited to, disks and optical discs. Communication unit 409 allows electronic device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0143] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the aircraft state estimation method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via ROM 402 and / or communication unit 409. In some embodiments, the computing unit 401 can be configured to perform the aircraft state estimation method by any other suitable means (e.g., by means of firmware).

[0144] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0145] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0146] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0147] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0148] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0149] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0150] Furthermore, it should be understood that the above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for estimating the state of an aircraft, characterized in that, include: Acquire the current waypoint data of the target aircraft. The current waypoint data includes the current event time, the current field pattern, and the current source identifier, which is used to indicate the current data source. When it is detected that the current waypoint data satisfies the global state candidate update condition of the target aircraft, the historical global interactive multi-model state of the target aircraft under the previous valid event time is determined, and the historical current source measurement noise fully statistical state of the target aircraft under the previous valid event time, the current data source, and the current field mode is determined; wherein, an event time in the waypoint data used to update the global state of the target aircraft is regarded as a valid event time of the target aircraft; Based on the current trackpoint data and the sufficient statistical status of the historical current source measurement noise, the historical measurement noise of the current source is determined; and based on the historical measurement noise of the current source, the applicable measurement noise of the current source is determined. Based on the current applicable measurement noise from the current source and the historical global interactive multi-model state, the current global interactive multi-model state of the target aircraft at the current event time is determined; and based on the current global interactive multi-model state, the current global state of the target aircraft at the current event time is determined; the current global state supports updating the global estimated trajectory of the target aircraft. Based on the current applicable source measurement noise, a sufficiently statistical state of the source measurement noise of the target aircraft at the current event time is determined for use in the state estimation of the target aircraft at the next valid event time.

2. The method according to claim 1, characterized in that, A sufficient statistical state of source measurement noise includes at least one model metric parameter and the time of the most recent valid update event; determining the historical measurement noise of the current source based on the current waypoint data and the historical sufficient statistical state of the current source measurement noise includes: From the fully statistical state of the historical current source measurement noise, determine the time of the most recent valid update event of the current source; and based on the difference between the current event time and the most recent valid update event time of the current source, determine the current source attenuation factor; According to the current source attenuation factor, the model metric parameter in the fully statistical state of the historical current source measurement noise is attenuated to obtain at least one attenuated model metric parameter. Determine the prior measurement noise covariance of the current data source and the current field mode, and determine the historical measurement noise of the current source based on the prior measurement noise covariance and the at least one attenuation model metric parameter.

3. The method according to claim 1 or 2, characterized in that, The step of determining the applicable measurement noise for the current source based on the historical measurement noise of the current source includes: Determine the set of compatible source statistical metadata corresponding to the current statistical metadata consisting of the current data source and the current field pattern. The data source in a set of compatible source statistical metadata is a data source other than the current data source in the source set. Based on the sufficient statistical state of the source measurement noise of the target aircraft under each compatible source statistical metadata in the previous valid event time and the compatible source statistical metadata set, the source historical measurement noise of the target aircraft under the current event time and each compatible source statistical metadata is determined. Based on the target aircraft's historical measurement noise at the current event time and under the statistical metadata of each compatible source, a source external reference matrix is ​​determined; and based on the source external reference matrix and the current source historical measurement noise, the applicable measurement noise for the current source is determined.

4. The method according to claim 3, characterized in that, The current waypoint data also includes external measurement uncertainty parameters; determining the applicable measurement noise for the current source based on the source external reference matrix and the current source historical measurement noise includes: Based on the external measurement uncertainty parameters, determine the current reporting accuracy matrix; Based on the current reported accuracy matrix, the external reference matrix of the source, and the historical measurement noise of the current source, the applicable measurement noise of the current source is determined.

5. The method according to claim 1 or 2, characterized in that, A global interactive multi-model state includes the posterior motion state of each motion model in the motion model set, the posterior state estimation error covariance, and the model posterior probability; determining the current global interactive multi-model state of the target aircraft at the current event time based on the current source applicable measurement noise and the historical global interactive multi-model states includes: Determine the model transition matrix, which includes the model transition probabilities between any two motion models in the set of motion models; Based on the historical model posterior probability of each motion model in the historical global interactive multi-model state and the model transition matrix, the interaction probability of each motion model is determined. The interaction probability of any motion model includes the conditional probability of the previous valid event time using each motion model under the condition that the current event time uses any of the motion models. Based on the interaction probabilities of each motion model, the mixed initial state and mixed initial covariance of each motion model are determined respectively; and based on the mixed initial state, mixed initial covariance of each motion model and the applicable measurement noise from the current source, the model innovation and model innovation covariance of each motion model are determined respectively. Based on the model innovation and model innovation covariance of each motion model, the posterior motion state, posterior state estimation error covariance, and model posterior probability of each motion model at the current event time are determined, so as to determine the current global interactive multi-model state of the target aircraft at the current event time.

6. The method according to claim 5, characterized in that, The determination of the posterior motion state, posterior state estimation error covariance, and posterior probability of each motion model at the current event time, based on the model innovation and model innovation covariance of each motion model, includes: For the m-th motion model in the set of motion models, the Kalman gain of the m-th motion model is determined based on the model innovation covariance of the m-th motion model; and the posterior motion state of the m-th motion model at the current event time is determined based on the Kalman gain and model innovation of the m-th motion model. Based on the Kalman gain of the m-th motion model and the applicable measurement noise from the current source, determine the posterior state estimation error covariance of the m-th motion model at the current event time; Based on the model innovation covariance and model innovation of the m-th motion model, the model likelihood of the m-th motion model is determined; and based on the model likelihood of the m-th motion model, the posterior probability of the m-th motion model is determined.

7. The method according to claim 5, characterized in that, The step of determining the sufficiently statistical state of the source measurement noise of the target aircraft at the current event time based on the applicable source measurement noise includes: Based on the predicted measurements of each motion model, a hybrid predicted measurement is determined; The difference between the current measurement vector in the current waypoint data and the hybrid prediction measurement is used as the hybrid prediction information; and the hybrid prediction uncertainty is determined based on the hybrid prediction measurement and the prediction measurement of each motion model. Based on the applicable measurement noise from the current source, the mixed prediction uncertainty, and the mixed prediction innovation, determine the statistical weight at the current event time; Based on the statistical weights at the current event time, the source measurement noise sufficient statistical state of the target aircraft at the current data source, the current field mode, and the current event time is determined. The source measurement noise sufficient statistical state of the target aircraft at the current event time includes the source measurement noise sufficient statistical state of the target aircraft at the current data source, the current field mode, and the current event time.

8. An aircraft state estimation device, characterized in that, The device includes: The acquisition unit is used to acquire the current waypoint data of the target aircraft. The current waypoint data includes the current event time, the current field mode, and the current source identifier, which is used to indicate the current data source. The processing unit is configured to, when detecting that the current waypoint data satisfies the global state candidate update condition of the target aircraft, determine the historical global interactive multi-model state of the target aircraft at the previous valid event time, and determine the historical current source measurement noise sufficient statistical state of the target aircraft at the previous valid event time, the current data source, and the current field mode; wherein, an event time in the waypoint data used to update the global state of the target aircraft is regarded as a valid event time of the target aircraft; The processing unit is further configured to determine the historical measurement noise of the current source based on the current track point data and the sufficient statistical state of the historical current source measurement noise; and to determine the applicable measurement noise of the current source based on the historical measurement noise of the current source. The processing unit is further configured to determine the current global interactive multi-model state of the target aircraft at the current event time based on the current source applicable measurement noise and the historical global interactive multi-model state; and to determine the current global state of the target aircraft at the current event time based on the current global interactive multi-model state; the current global state supports updating the global estimated trajectory of the target aircraft; The processing unit is further configured to determine, based on the current applicable source measurement noise, the source measurement noise of the target aircraft at the current event time, to be used for state estimation of the target aircraft at the next valid event time.

9. An electronic device, characterized in that, include: processor; as well as Stored program memory, The program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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