Aircraft state estimation method and device, medium, equipment and product

Abstract

The invention relates to the technical field of aircraft navigation control, and particularly provides an aircraft state estimation method and device, a medium, equipment and a product, and the method can comprise the steps: obtaining an initial state variable of an aircraft; updating the initial state variable by using the motion acceleration of the aircraft under the navigation coordinate system to obtain updated state data; wherein the updated state data comprises an updated state variable and an error covariance matrix corresponding to the updated state variable; correcting the updated state data based on external observation data to obtain target state data of the aircraft; wherein the target state data comprises a target state variable and a target error covariance matrix. According to the embodiment of the invention, the accuracy of aircraft state estimation can be improved.

Description

Technical Field

[0001] This application relates to the field of aircraft navigation and control technology, and more specifically, to a method, apparatus, medium, equipment, and product for aircraft state estimation. Background Technology

[0002] Aircraft navigation and control is the core technology for aircraft to achieve autonomous flight, path planning, and mission execution. Its essence is to determine the aircraft's position, speed, attitude (PVQ) and other information in real time, and select an appropriate navigation scheme according to mission requirements.

[0003] Currently, aircraft navigation and control technologies employ either single-sensor navigation or multi-sensor fusion navigation. Single-sensor navigation relies primarily on satellite signals, requiring the deployment of fixed base stations, resulting in high deployment costs. Furthermore, it is susceptible to interference from buildings, tunnels, and other obstacles, making accurate assessment of the aircraft's status impossible. In multi-sensor fusion navigation, existing fusion algorithms (such as EKF) directly use new observations during sensor switching without compensating for coordinate system offsets, leading to low accuracy in the final acquired aircraft status.

[0004] Therefore, how to provide a more accurate method for estimating the state of an aircraft has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of some embodiments of this application is to provide a method, apparatus, medium, device and product for aircraft state estimation. The technical solutions of the embodiments of this application can improve the accuracy of aircraft state estimation and are applicable to various complex environmental scenarios.

[0006] In a first aspect, some embodiments of this application provide a method for aircraft state estimation, comprising: acquiring initial state variables of the aircraft; wherein the initial state variables include the aircraft's acceleration zero bias, velocity, position in the navigation coordinate system, visual position in the visual coordinate system, and yaw transformation angle between the visual coordinate system and the navigation coordinate system; updating the initial state variables using the aircraft's motion acceleration in the navigation coordinate system to acquire updated state data; wherein the updated state data includes the updated state variables and the error covariance matrix corresponding to the updated state variables; correcting the updated state data based on external observation data to obtain target state data of the aircraft; wherein the target state data includes target state variables and target error covariance matrix; the external observation data is acquired from a visual navigation inertial system or a navigation satellite system.

[0007] Some embodiments of this application update the initial state variables of the aircraft using its motion acceleration in the navigation coordinate system to obtain updated state data; then, additional external observation data is used to correct the updated state data to obtain the target state data of the aircraft. These embodiments can effectively suppress inertial navigation system position drift, improve the accuracy of aircraft state estimation, and are applicable to aircraft state estimation in various complex environmental scenarios.

[0008] In some embodiments, obtaining the initial state variables of the aircraft includes: initializing the aircraft's acceleration zero bias and velocity to set values; calculating the yaw angle of the aircraft and the yaw angle of the visual navigation inertial system at the same time to obtain the yaw conversion angle; and obtaining the visual position of the aircraft through the yaw conversion angle.

[0009] Some embodiments of this application provide effective support for subsequent state estimation of the aircraft by setting initial state variables of the aircraft.

[0010] In some embodiments, updating the initial state variables using the motion acceleration of the aircraft in the navigation coordinate system to obtain updated state data includes: inputting the motion acceleration into the discrete equations corresponding to a pre-constructed dynamic model to obtain the updated state variables; determining the state transition Jacobian matrix and the process noise covariance matrix corresponding to the updated state variables; and obtaining the error covariance matrix based on the state transition Jacobian matrix and the process noise covariance matrix.

[0011] Some embodiments of this application obtain updated state variables through motion acceleration and discrete equations, and then combine the state transition Jacobian matrix and process noise covariance matrix to obtain the error covariance matrix, thereby realizing the effective updating of aircraft state data.

[0012] In some embodiments, the step of correcting the updated state data based on external observation data to obtain the target state data of the aircraft includes: acquiring external observation data from an external observation system while the aircraft is in flight; wherein the external observation data includes position observation data, velocity observation data, observation Jacobian matrix, and observation noise covariance matrix; and correcting the updated state data using the position observation data, the velocity observation data, the observation Jacobian matrix, and the observation noise covariance matrix to obtain the target state data.

[0013] Some embodiments of this application correct the updated state data by using external observation data collected by an external observation system to obtain target state data, thereby achieving accurate estimation of the aircraft's state.

[0014] In some embodiments, acquiring the external observation data under an external observation system includes: if it is confirmed that the visual navigation inertial system has data updates, then using the visual navigation inertial system as the external observation system and acquiring the external observation data of the visual navigation inertial system in the visual coordinate system; if it is confirmed that the visual navigation inertial system has no data updates but the navigation satellite system has data updates, then using the navigation satellite system as the external observation system and acquiring the external observation data of the navigation satellite system in the navigation coordinate system.

[0015] Some embodiments of this application obtain target state data by correcting updated state data using external observation data from visual navigation inertial systems or navigation satellite systems under different conditions, thereby enabling accurate estimation of the aircraft state under different conditions.

[0016] In some embodiments, the step of correcting the updated state data based on external observation data to obtain the target state data of the aircraft includes: acquiring zero-velocity correction data when the aircraft is stationary and the external observation system is abnormal; wherein the zero-velocity correction data is used as the external observation data; the zero-velocity correction data includes velocity data, Jacobian matrix, and noise covariance matrix in the navigation coordinate system; and correcting the updated state data using the velocity data, the Jacobian matrix, and the noise covariance matrix to obtain the target state data.

[0017] Some embodiments of this application use zero-velocity correction data to correct the updated state data when the aircraft is confirmed to be stationary and the external observation system is abnormal, thereby obtaining target state data. This enables accurate estimation of the aircraft state under special circumstances.

[0018] In some embodiments, the method further includes: when the aircraft is in flight and the external observation system is malfunctioning, using the updated state data as the target state data.

[0019] In some embodiments, before obtaining the initial state variables of the aircraft, the method further includes: defining the variable type of the initial state variables and constructing the dynamic model; processing the dynamic model to obtain the discrete equations.

[0020] Some embodiments of this application provide support for subsequent aircraft estimation by defining the variable types of initial state variables and constructing a dynamic model.

[0021] Secondly, some embodiments of this application provide an apparatus for aircraft state estimation, comprising: an initialization module for acquiring initial state variables of the aircraft; wherein the initial state variables include the aircraft's acceleration zero bias, velocity, position in the navigation coordinate system, visual position in the visual coordinate system, and yaw transformation angle between the visual coordinate system and the navigation coordinate system; an update module for updating the initial state variables using the aircraft's motion acceleration in the navigation coordinate system to acquire updated state data; wherein the updated state data includes the updated state variables and the error covariance matrix corresponding to the updated state variables; and a correction module for correcting the updated state data based on external observation data to obtain target state data of the aircraft; wherein the target state data includes target state variables and a target error covariance matrix; the external observation data is acquired from a visual navigation inertial system or a navigation satellite system.

[0022] Thirdly, some embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the method described in any embodiment of the first aspect.

[0023] Fourthly, some embodiments of this application provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, can implement the method as described in any embodiment of the first aspect.

[0024] Fifthly, some embodiments of this application provide a computer program product, the computer program product including a computer program, wherein the computer program, when executed by a processor, can implement the method described in any embodiment of the first aspect. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of some embodiments of this application, the accompanying drawings used in some embodiments of this application will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 One of the flowcharts for a method of aircraft state estimation provided for some embodiments of this application; Figure 2 A second flowchart of a method for aircraft state estimation provided for some embodiments of this application; Figure 3Block diagram of an apparatus for aircraft state estimation provided for some embodiments of this application; Figure 4 A schematic diagram of an electronic device provided for some embodiments of this application. Detailed Implementation

[0027] The technical solutions of some embodiments of this application will now be described with reference to the accompanying drawings.

[0028] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] In related technologies, aircraft state estimation can be achieved using a single sensor or a multi-sensor approach. A single-sensor approach can include simple GNSS (Global Navigation Satellite System) positioning and visual positioning. GNSS positioning relies on satellite signals but is prone to failure in scenarios with building obstructions or tunnels; moreover, RTK (Real-Time Kinematic) requires fixed base stations, resulting in high deployment costs; and single-point GPS positioning errors can reach the meter level. In short, GNSS positioning cannot handle signal loss scenarios and has poor dynamic performance. Visual positioning alone relies on environmental texture features, is prone to failure in low light or motion blur conditions, suffers from severe cumulative drift, and exhibits error divergence over long periods, lacking absolute position references and hindering global convergence. Multi-sensor solutions require switching between multiple sensors; existing multi-sensor fusion algorithms can directly use new observations during sensor switching, which can easily lead to uncompensated coordinate system offsets, causing abrupt changes in estimated position, triggering aircraft attitude oscillations or even loss of control.

[0030] Furthermore, relative sensors (e.g., vision sensors) can provide relative position with respect to an initial moment or a local coordinate system. Vision systems typically establish a local coordinate system (visual coordinate system) with a rotational and translational relationship to the global navigation coordinate system. In existing technologies, the transformation parameters (rotation angle, translation amount) between the visual and navigation coordinate systems are usually assumed to be fixed or obtained through initial calibration. However, in actual operation, due to factors such as sensor installation errors, measurement accuracy, and temperature variations, the transformation parameters can change, leading to a significant final fusion error.

[0031] In view of this, some embodiments of this application provide a method for aircraft state estimation. This method, after obtaining the initial state variables of the aircraft, updates the state data by using the aircraft's motion acceleration in the navigation coordinate system. Finally, it corrects the updated state data using external observation data from an external observation system to obtain the target state data of the aircraft. Embodiments of this application can process the state data based on sensor availability, improving positioning robustness in complex environments; by determining the yaw conversion angle, high-precision fusion across coordinate systems can be achieved, improving the accuracy of aircraft state estimation.

[0032] In some embodiments of this application, the aircraft can be various types of drones or other aerial flying devices. For ease of explanation, the implementation process of this application will be illustrated below using a drone as an example.

[0033] The following is in conjunction with the appendix Figure 1 The implementation process of the aircraft state estimation method provided in some embodiments of this application is illustrated by way of example. The implementation process of the aircraft state estimation can be executed by the processor deployed on the UAV itself, or by the terminal connected to the UAV for communication. The embodiments of this application do not make specific limitations here.

[0034] Please see the appendix Figure 1 , Figure 1 A flowchart of a method for aircraft state estimation provided for some embodiments of this application.

[0035] In some embodiments of this application, before performing the following aircraft state estimation implementation process, it is necessary to predefine the coordinate system and parameters in the implementation process.

[0036] Specifically, the following implementation involves three coordinate systems: the body coordinate system, the navigation coordinate system, and the visual coordinate system. The body coordinate system uses the FRD (Forward-Right-Down) coordinate system, with the positive X-axis pointing towards the nose, the positive Y-axis pointing towards the right side of the fuselage, and the positive Z-axis pointing downwards from the fuselage; this can be simply referred to as the B-system. The navigation coordinate system uses the NED (North-East-Down) coordinate system, with the positive X-axis pointing towards the geographic North Pole, the positive Y-axis pointing towards geographic east, and the positive Z-axis pointing towards the Earth's center; this can be simply referred to as the N-system. The visual coordinate system, after a rotation angle (or transformation angle, yaw transformation angle, etc.), can be aligned with the N-system; this can be simply referred to as the V-system.

[0037] In some embodiments of this application, before performing S110, the method for estimating the aircraft state may include: defining the variable type of the initial state variable and constructing the dynamic model; processing the dynamic model to obtain the discrete equation.

[0038] For example, in a specific example of this application, the state variable x of the UAV is first defined: ,in, For zero bias acceleration in the N-system, For velocity in the N-system, Position in the N-system Visual position under the V-frame. This is the transition angle from the V-system to the N-system.

[0039] Construct a system dynamics model (referred to as the dynamics model) as shown in the following formula (1): (1) in, The acceleration in the N-frame can be obtained by rotating and compensating for gravity after taking the accelerometer specific force measurement in the B-frame; the acceleration has zero bias. Visual position drift and the transition angle from the V-series to the N-series All are modeled as random walk processes. By performing a Taylor series expansion on the above formulas, the following discrete equation (2) is obtained: (2) In some embodiments of this application, the method for estimating the aircraft state may include: S110, Obtain the initial state variables of the aircraft; wherein, the initial state variables include the aircraft's zero acceleration bias, velocity, position in the navigation coordinate system, visual position in the visual coordinate system, and yaw conversion angle between the visual coordinate system and the navigation coordinate system.

[0040] For example, in a specific embodiment of this application, the state variables defined above are initialized to obtain initial state variables.

[0041] In some embodiments of this application, S110 may include: initializing the acceleration zero bias and velocity of the aircraft to set values; calculating the yaw angle of the aircraft and the yaw angle of the visual navigation inertial system at the same time to obtain the yaw conversion angle; and obtaining the visual position of the aircraft through the yaw conversion angle.

[0042] For example, in a specific embodiment of this application, the acceleration in the N-system is zero biased. and speed Initialize to zero (as a specific example of a set value). The yaw angle initialization from V-series to N-series is based on the yaw angle of the UAV at the first simultaneous moment. and VINS (Visual-Inertial Navigation System) yaw angle Calculations were performed to obtain the yaw conversion angle. ,Right now .

[0043] The visual position is initialized according to the following formula:

[0044] in, These are the initial positions in the N-series and V-series, respectively.

[0045] S120, using the motion acceleration of the aircraft in the navigation coordinate system, the initial state variables are updated to obtain updated state data; wherein, the updated state data includes the updated state variables and the error covariance matrix corresponding to the updated state variables.

[0046] For example, in a specific embodiment of this application, the prediction step of the standard Kalman filter is used as the core to predict and update the initial state variables and the error covariance matrix. The specific update formula is as follows (3): (3) in, For the updated state variables, This is the updated error covariance matrix.

[0047] In some embodiments of this application, S120 may include: inputting the motion acceleration into the discrete equations corresponding to the pre-constructed dynamic model to obtain the updated state variables; determining the state transition Jacobian matrix and the process noise covariance matrix corresponding to the updated state variables; and obtaining the error covariance matrix based on the state transition Jacobian matrix and the process noise covariance matrix.

[0048] For example, in a specific embodiment of this application, the nine initial state variables defined above are first updated, and the motion acceleration in the N-system is... Inputting the data into formula (2) yields the updated state variables. Next, obtain the state transition Jacobian matrix. process noise covariance matrix :

[0049]

[0050] in, Let I be the variance of acceleration zero bias, velocity, position in the N-frame, visual position, and transformation angle, and let I be the identity matrix.

[0051] Will and Substituting these values ​​into formula (3) yields the updated error covariance matrix.

[0052] S130, the updated state data is corrected based on external observation data to obtain the target state data of the aircraft; wherein, the target state data includes target state variables and target error covariance matrix; the external observation data is obtained from a visual navigation inertial system or a navigation satellite system.

[0053] For example, in a specific embodiment of this application, other sensor data (as a specific example of external observation data) are used to correct the updated state variables and error covariance matrix obtained above, resulting in a more accurate state estimate (as a specific example of target state data). The other sensor data can include three categories: data obtained from VINS position observations in the V-frame, data obtained from GNSS position observations in the N-frame, and zero-velocity corrected data. For any type of sensor data, the state variables and error covariance matrix can be corrected according to the formula in the following update steps of the standard Kalman filter: (4) Where H is the observation Jacobian matrix, R is the observation noise covariance matrix, z is the matrix composed of position and velocity observation data, and I is the identity matrix.

[0054] In some embodiments of this application, S130 may include: when the aircraft is in flight, acquiring the external observation data from an external observation system; wherein the external observation data includes position observation data, velocity observation data, observation Jacobian matrix, and observation noise covariance matrix; and correcting the updated state data using the position observation data, the velocity observation data, the observation Jacobian matrix, and the observation noise covariance matrix to obtain the target state data.

[0055] For example, in a specific embodiment of this application, a correction operation is performed when VINS or GNSS data is refreshed during the flight of the UAV. Since the coordinate systems of the two observations are different, their observation Jacobian matrix and observation noise covariance matrix are also different. After obtaining the external observation data under VINS or GNSS, the data is input into formula (4) to correct the updated state data and obtain the final state estimate.

[0056] In some embodiments of this application, S130 may include: if it is confirmed that there is a data update in the visual navigation inertial system, then using the visual navigation inertial system as the external observation system, and acquiring the external observation data of the visual navigation inertial system in the visual coordinate system.

[0057] For example, in a specific embodiment of this application, when it is determined that VINS is being refreshed, it is used as an external observation system to obtain the corresponding external observation data.

[0058] Among them, the position observation data and velocity observation data are z vins : ,in, ; Observation Jacobian matrix H vins :

[0059] in, , , , .

[0060] Observation noise covariance matrix R vins : .

[0061] In some embodiments of this application, S130 may include: if it is confirmed that the visual navigation inertial system does not have data updates but the navigation satellite system does have data updates, then the navigation satellite system is used as the external observation system, and the external observation data of the navigation satellite system in the navigation coordinate system is acquired.

[0062] For example, in a specific embodiment of this application, when it is determined that there is a data refresh in the GNSS, it is used as an external observation system to obtain the corresponding external observation data.

[0063] Among them, the position observation data and velocity observation data are z gnss : ; Observation Jacobian matrix H gnss : ; Observation noise covariance matrix R gnss : .

[0064] In some embodiments of this application, S130 may include: acquiring zero-velocity correction data when the aircraft is stationary and the external observation system is abnormal; wherein the zero-velocity correction data is the external observation data; the zero-velocity correction data includes velocity data, Jacobian matrix, and noise covariance matrix in the navigation coordinate system; and correcting the updated state data using the velocity data, the Jacobian matrix, and the noise covariance matrix to obtain the target state data.

[0065] For example, in a specific embodiment of this application, when both GNSS and VINS data are unavailable (i.e., when the external observation system is malfunctioning) and the UAV is stationary, zero-velocity correction is enabled. At this time, velocity data in the N-series can be obtained: .

[0066] Observation Jacobian matrix H zero :

[0067] Observation noise covariance matrix R zero : .

[0068] As can be seen, the above implementation process is as follows: when VINS has observation data refreshed, the VINS observation value z is... vins H vins R vins Substituting these values ​​into formula (4) completes the correction of the state variables and the error covariance matrix. Similarly, when GNSS observation data is refreshed, the GNSS observation z... gnss H gnss R gnss Substituting these values ​​into formula (4) completes the correction of the state variables and the error covariance matrix. When both VINS and GNSS are unavailable and the UAV is stationary, zero-velocity correction is enabled, and z... zero H zero R zero Substituting these values ​​into formula (4) completes the correction of the state variables and the error covariance matrix.

[0069] In addition, when the aircraft is in flight and the external observation system is malfunctioning, the updated status data will be used as the target status data.

[0070] The following is in conjunction with the appendix Figure 2 The present application provides an exemplary description of the specific process of aircraft state estimation provided by some embodiments of this application.

[0071] Please see the appendix Figure 2 , Figure 2A flowchart of a method for aircraft state estimation provided for some embodiments of this application.

[0072] The above process is illustrated below by example.

[0073] S210, define the variable types of the initial state variables and construct the dynamic model; process the dynamic model to obtain discrete equations.

[0074] S220, Initialize the initial state variables.

[0075] S230: Input the UAV's motion acceleration into the discrete equation to obtain the updated state variables; based on the state transition Jacobian matrix and the process noise covariance matrix, obtain the error covariance matrix.

[0076] S240: Determine if VINS has been refreshed. If so, execute S270; otherwise, execute S250.

[0077] S250: Determine if there is a data refresh in GNSS. If yes, proceed to S270; otherwise, proceed to S260.

[0078] S260: Determine if the drone is stationary. If yes, execute S270; otherwise, execute S280.

[0079] S270: Obtain the current external observation data, and use the external observation data to correct the updated state variables and error covariance matrix to obtain the state estimate of the UAV.

[0080] S280, use the updated state variables as state estimates.

[0081] It is understood that the specific implementation process of S210~S280 can be referred to the method embodiment provided above. To avoid repetition, detailed descriptions are omitted here.

[0082] As can be seen from the above embodiments of this application, by explicitly modeling visual position drift and coordinate system transformation angle, this application can estimate the rotation and translation parameters from the visual to the navigation coordinate system in real time, which can significantly improve the positioning accuracy under poor GNSS data conditions; at the same time, the coordinated correction of VINS, GNSS and zero velocity correction can effectively suppress inertial navigation position drift.

[0083] Please refer to Figure 3 , Figure 3 The diagram illustrates a block diagram of an aircraft state estimation apparatus provided in some embodiments of this application. It should be understood that this aircraft state estimation apparatus corresponds to the method embodiments described above and is capable of performing the various steps involved in the method embodiments. The specific functions of this aircraft state estimation apparatus can be found in the description above; detailed descriptions are omitted here to avoid repetition.

[0084] Figure 3 The aircraft state estimation device includes at least one software functional module that can be stored in a memory or embedded in the aircraft state estimation device in the form of software or firmware. The device includes: an initialization module 310 for acquiring initial state variables of the aircraft; wherein the initial state variables include the aircraft's acceleration zero bias, velocity, position in the navigation coordinate system, visual position in the visual coordinate system, and yaw transformation angle between the visual coordinate system and the navigation coordinate system; an update module 320 for updating the initial state variables using the aircraft's motion acceleration in the navigation coordinate system to acquire updated state data; wherein the updated state data includes the updated state variables and the corresponding error covariance matrix; and a correction module 330 for correcting the updated state data based on external observation data to obtain target state data of the aircraft; wherein the target state data includes target state variables and a target error covariance matrix; the external observation data is acquired from a visual navigation inertial system or a navigation satellite system.

[0085] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the aforementioned method, and will not be elaborated further here.

[0086] Some embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can perform the operation of any of the methods corresponding to the methods provided in the above embodiments.

[0087] Some embodiments of this application also provide a computer program product, which includes a computer program, wherein when the computer program is executed by a processor, it can implement the operation of any of the methods corresponding to the above embodiments provided in the above embodiments.

[0088] like Figure 4 As shown, some embodiments of this application provide an electronic device 400, which includes a memory 410, a processor 420, and a computer program stored in the memory 410 and executable on the processor 420. When the processor 420 reads the program from the memory 410 via a bus 430 and executes the program, it can implement the methods of any of the above embodiments.

[0089] Processor 420 can process digital signals and may include various computing architectures. For example, it may be a complex instruction set computer architecture, a reduced instruction set computer architecture, or an architecture that implements multiple instruction set combinations. In some examples, processor 420 may be a microprocessor.

[0090] Memory 410 can be used to store instructions executed by processor 420 or data related to the execution of instructions. These instructions and / or data may include code for implementing some or all of the functions of one or more modules described in the embodiments of this application. The processor 420 of this disclosure embodiment can be used to execute instructions in memory 410 to implement the methods shown above. Memory 410 includes dynamic random access memory, static random access memory, flash memory, optical memory, or other memories well known to those skilled in the art.

[0091] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0092] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0093] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for aircraft state estimation, characterized in that, include: Obtain the initial state variables of the aircraft; wherein, the initial state variables include the aircraft's zero acceleration bias, velocity, position in the navigation coordinate system, visual position in the visual coordinate system, and yaw conversion angle between the visual coordinate system and the navigation coordinate system; The initial state variables are updated using the motion acceleration of the aircraft in the navigation coordinate system to obtain updated state data; wherein, the updated state data includes the updated state variables and the error covariance matrix corresponding to the updated state variables; The updated state data of the aircraft is corrected based on external observation data to obtain target state data; wherein, the target state data includes target state variables and target error covariance matrix; the external observation data is obtained from a visual navigation inertial system or a navigation satellite system.

2. The method as described in claim 1, characterized in that, The acquisition of the initial state variables of the aircraft includes: The acceleration bias and velocity of the aircraft are initialized to set values; The yaw angle is obtained by calculating the yaw angle of the aircraft and the yaw angle of the visual navigation inertial system at the same time. The visual position of the aircraft is obtained by the yaw conversion angle.

3. The method as described in claim 1 or 2, characterized in that, The step of updating the initial state variables using the motion acceleration of the aircraft in the navigation coordinate system to obtain updated state data includes: The motion acceleration is input into the discrete equations corresponding to the pre-constructed dynamic model to obtain the updated state variables; Determine the state transition Jacobian matrix and process noise covariance matrix corresponding to the updated state variables; The error covariance matrix is ​​obtained based on the state transition Jacobian matrix and the process noise covariance matrix.

4. The method as described in claim 1 or 2, characterized in that, The process of correcting the updated state data based on external observation data to obtain the target state data of the aircraft includes: While the aircraft is in flight, external observation data from an external observation system is acquired; wherein, the external observation data includes position observation data, velocity observation data, observation Jacobian matrix, and observation noise covariance matrix; The updated state data is corrected using the position observation data, the velocity observation data, the observation Jacobian matrix, and the observation noise covariance matrix to obtain the target state data.

5. The method as described in claim 4, characterized in that, The acquisition of the external observation data from the external observation system includes: If it is confirmed that the visual navigation inertial system has data updates, then the visual navigation inertial system is used as the external observation system, and the external observation data of the visual navigation inertial system in the visual coordinate system is acquired. If it is confirmed that the visual navigation inertial system does not have data updates, but the navigation satellite system does have data updates, then the navigation satellite system is used as the external observation system, and the external observation data of the navigation satellite system in the navigation coordinate system is acquired.

6. The method as described in claim 1 or 2, characterized in that, The process of correcting the updated state data based on external observation data to obtain the target state data of the aircraft includes: When the aircraft is stationary and the external observation system is malfunctioning, zero-velocity correction data is acquired; wherein, the zero-velocity correction data is used as the external observation data; the zero-velocity correction data includes velocity data, Jacobian matrix, and noise covariance matrix in the navigation coordinate system; The updated state data is corrected using the velocity data, the Jacobian matrix, and the noise covariance matrix to obtain the target state data.

7. The method as described in claim 1 or 2, characterized in that, The method further includes: If the aircraft is in flight and the external observation system is malfunctioning, the updated status data will be used as the target status data.

8. The method as described in claim 3, characterized in that, Before acquiring the initial state variables of the aircraft, the method further includes: Define the variable types of the initial state variables and construct the dynamic model; The dynamic model is processed to obtain the discrete equations.

9. An apparatus for estimating the state of an aircraft, characterized in that, include: An initialization module is used to obtain the initial state variables of the aircraft; wherein, the initial state variables include the aircraft's zero acceleration bias, velocity, position in the navigation coordinate system, visual position in the visual coordinate system, and yaw conversion angle between the visual coordinate system and the navigation coordinate system; An update module is used to update the initial state variables using the motion acceleration of the aircraft in the navigation coordinate system to obtain updated state data; wherein, the updated state data includes the updated state variables and the error covariance matrix corresponding to the updated state variables; The correction module is used to correct the updated state data based on external observation data to obtain the target state data of the aircraft; wherein, the target state data includes target state variables and target error covariance matrix; the external observation data is obtained from a visual navigation inertial system or a navigation satellite system.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is executed by a processor to perform the method as described in any one of claims 1-8.

11. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and running on the processor, wherein the computer program is executed by the processor to perform the method as described in any one of claims 1-8.

12. A computer program product, characterized in that, The computer program product includes a computer program, wherein the computer program is executed by a processor to perform the method as described in any one of claims 1-8.

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