An optimal estimation method for satellite orbit prediction

Abstract

The present disclosure provides a method for optimal estimation of satellite orbit prediction value, comprising: obtaining inter-satellite distance representation data for satellite relative position constraint, the inter-satellite distance representation data being determined by a plurality of pairs of satellite group corresponding position motion parameters, the position motion parameters being determined by an inter-satellite distance observation calculation model; obtaining satellite state vector representation, the satellite state vector representation being used to describe a plurality of satellite corresponding single state vector representation, the single state vector representation being used to describe a corresponding satellite position vector and a corresponding satellite velocity vector; using a preset filtering algorithm, performing state vector optimization on the satellite state vector representation by the inter-satellite distance representation data for satellite relative position constraint, to obtain an optimal estimation result of the satellite orbit prediction value, the optimal estimation result being used to describe an optimal estimation position and an optimal estimation velocity of the satellite. Thus, the satellite orbit prediction accuracy is effectively improved.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the fields of communication, navigation, and remote sensing; more specifically, they relate to an optimal estimation method applicable to a satellite orbit prediction. Background Technology

[0002] Satellite orbit prediction is crucial for navigation and positioning systems. The autonomous navigation capability of navigation satellites depends on accurate orbit prediction to ensure that satellites can independently set and maintain their own orbits based on their own measurements and calculations. Orbit accuracy directly affects the positioning accuracy of navigation satellites, and thus the continuous operation and reliability of the navigation system.

[0003] In related technologies, satellite orbit prediction typically uses mathematical models and algorithms to estimate the satellite's position and velocity at future points in time. For example, a photometric signal observation model is established using the surface elements of the observed satellite's geometric model through a BRDF (Bidirectional Reflectance Distribution Function); based on the observed satellite's orbital position and instantaneous velocity, the orbital kinematic equations are determined using Newton's two-body gravitational equations; and using the orbital kinematic equations and the photometric signal observation model, a lossless Kalman filter algorithm is employed to predict the satellite orbital parameters at the next observation time.

[0004] However, with existing technology, the accuracy of satellite orbit prediction is not high. Summary of the Invention

[0005] The embodiments described herein provide an optimal estimation method for satellite orbit predictions that overcomes the aforementioned problems.

[0006] Firstly, based on the content of this disclosure, an optimal estimation method for satellite orbit predictions is provided, including:

[0007] The inter-satellite spacing data used for satellite relative position constraints is obtained. The inter-satellite spacing data is determined by the position motion parameters corresponding to multiple pairs of satellite groups. The position motion parameters are determined by the inter-satellite spacing observation and calculation model.

[0008] Obtain satellite state vector representations, which are used to describe individual state vector representations corresponding to multiple satellites, and the individual state vector representations are used to describe the position vector and velocity vector of the corresponding satellite.

[0009] A preset filtering algorithm is used to optimize the satellite state vector representation using the inter-satellite spacing data used for satellite relative position constraints, thereby obtaining the optimal estimation result of the satellite orbit prediction value. The optimal estimation result is used to describe the optimal estimated position and optimal estimated velocity of the satellite.

[0010] Optionally, acquiring the inter-satellite spacing representation data for satellite relative position constraints includes:

[0011] Obtain coordinate observation data of multiple satellites from the navigation message of the GNSS receiver;

[0012] Multiple pairs of satellites are formed by multiple satellites, and the coordinate observation data corresponding to each pair of satellites is input into the inter-satellite spacing observation calculation model. The position and motion parameters corresponding to the multiple pairs of satellites are determined based on the output of the inter-satellite spacing observation calculation model.

[0013] By adding preset observation noise data to the position motion parameters corresponding to multiple pairs of satellite groups, the inter-satellite spacing observation data corresponding to each pair of satellite groups can be obtained.

[0014] The inter-satellite spacing representation data, used for constraining the relative positions of satellites, is constructed using the inter-satellite spacing observation data corresponding to each pair of satellite groups.

[0015] Optionally, obtaining the satellite state vector representation includes:

[0016] Obtain the position vectors and velocity vectors corresponding to multiple satellites, and determine the state representation data corresponding to the satellites using the position vectors and velocity vectors corresponding to the satellites;

[0017] Construct a single-item state vector representation of the satellite based on the state representation data corresponding to the satellite;

[0018] A global state transformation is performed on the individual state vector representation corresponding to each satellite to obtain the satellite state vector representation.

[0019] Optionally, constructing a single-item state vector representation of the satellite based on the state representation data corresponding to the satellite includes:

[0020] Acquire preset process noise data for satellite status representation;

[0021] A satellite dynamic equation representation is constructed based on the state representation data corresponding to the satellite;

[0022] The satellite's state vector representation is constructed using the satellite dynamics equations and the preset process noise data used for satellite state representation.

[0023] Optionally, the step of employing a preset filtering algorithm to optimize the satellite state vector representation using the inter-satellite spacing data used for satellite relative position constraints, thereby obtaining the optimal estimation result of the satellite orbit prediction value, includes:

[0024] State transition prediction is performed on the satellite state vector representation to obtain the transition state vector representation and covariance vector representation corresponding to the satellite state vector representation; and the position observation parameters corresponding to the transition from the satellite state vector representation to the transition state vector representation are determined by the inter-satellite distance observation calculation model.

[0025] An observation residual representation is constructed using the interstellar distance representation data and the position observation parameters. The transition state vector representation is updated using the observation residual representation. The covariance vector representation is updated using the Kalman gain data and the observation matrix representation to obtain the optimal estimation result of the satellite orbit prediction.

[0026] The observation matrix is ​​determined by the position observation parameters and the transition state vector.

[0027] Optional, also includes:

[0028] Obtain satellite orbit prediction models;

[0029] The satellite state vector representation is input into the satellite orbit prediction model, and the predicted values ​​of motion parameters are determined based on the output of the satellite orbit prediction model.

[0030] Based on the optimal estimation results of the predicted motion parameters and the predicted satellite orbit, the satellite orbit prediction model is optimized.

[0031] Optional, also includes:

[0032] Receive a request to acquire satellite orbit motion data, and determine a satellite orbit target reference value by using the optimal estimation result of the satellite orbit prediction value and the predicted value of the motion parameters determined by the satellite orbit prediction model;

[0033] In response to the request to acquire the satellite orbit motion data, the system sends the satellite orbit target reference value, the optimal estimation result of the satellite orbit prediction value, and the predicted value of the motion parameters to the service node device.

[0034] Secondly, according to the present disclosure, an optimal estimation device for satellite orbit prediction values ​​is provided, comprising:

[0035] The first acquisition module is used to acquire inter-satellite spacing representation data for satellite relative position constraints. The inter-satellite spacing representation data is determined by the position motion parameters corresponding to multiple pairs of satellite groups. The position motion parameters are determined by the inter-satellite spacing observation and calculation model.

[0036] The second acquisition module is used to acquire satellite state vector representations, which are used to describe individual state vector representations corresponding to multiple satellites, and the individual state vector representations are used to describe the position vector and velocity vector of the corresponding satellite.

[0037] The optimization module is used to perform state vector optimization on the satellite state vector representation using the inter-satellite spacing representation data used for satellite relative position constraints, by employing a preset filtering algorithm, to obtain the optimal estimation result of the satellite orbit prediction value. The optimal estimation result is used to describe the optimal estimated position and optimal estimated velocity of the satellite.

[0038] Thirdly, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the optimal estimation method for satellite orbit prediction values ​​as described in any of the above embodiments.

[0039] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, and when executed by a processor, the computer program implements the steps of the optimal estimation method for satellite orbit prediction values ​​as described in any of the above embodiments.

[0040] The optimal estimation method for satellite orbit prediction provided in this application involves obtaining inter-satellite spacing data for constraining the relative positions of satellites. This inter-satellite spacing data is determined by the position and motion parameters corresponding to multiple pairs of satellite groups, which are determined by an inter-satellite spacing observation and calculation model. The method also involves obtaining satellite state vector representations, which describe the individual state vector representations of multiple satellites, and these individual state vector representations describe the position and velocity vectors of the corresponding satellites. A preset filtering algorithm is then used to optimize the satellite state vector representations using the inter-satellite spacing data for constraining the relative positions of satellites, resulting in the optimal estimation result for the satellite orbit prediction. This optimal estimation result describes the optimal estimated position and optimal estimated velocity of the satellite. Thus, by introducing inter-satellite spacing as an orbital dynamic constraint as an observation to constrain the relative positions of satellites, the satellite state vectors are optimized, effectively improving the accuracy of satellite orbit prediction.

[0041] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments will be briefly described below. It should be understood that the drawings described below only relate to some embodiments of this disclosure and are not intended to limit this disclosure, wherein:

[0043] Figure 1 This is a flowchart illustrating an optimal estimation method for satellite orbit prediction provided in this disclosure.

[0044] Figure 2 This is a schematic diagram of the structure of an optimal estimation device for satellite orbit prediction provided in this disclosure.

[0045] Figure 3 This is a schematic diagram of the structure of a computer device provided in this disclosure.

[0046] It should be noted that the elements in the attached diagram are schematic and not drawn to scale. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are also within the scope of protection of this disclosure.

[0048] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this subject matter pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having the meaning consistent with their meaning in the context of the specification and in the relevant art, and shall not be interpreted in an idealized or overly formal form unless otherwise explicitly defined herein. As used herein, the statement of “connecting” or “coupling” two or more parts together shall mean that these parts are directly joined together or joined through one or more intermediate components.

[0049] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of the phrase "embodiment" in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0050] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists, A and B exist simultaneously, or B exists. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Terms such as "first" and "second" are only used to distinguish one component (or part of a component) from another component (or another part of a component).

[0051] In the description of this application, unless otherwise stated, "multiple" means two or more (including two), and similarly, "multiple groups" means two or more (including two groups).

[0052] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0053] Figure 1 This is a flowchart illustrating an optimal estimation method for satellite orbit prediction provided in an embodiment of this disclosure, as shown below. Figure 1 As shown, the specific process of the optimal estimation method for satellite orbit predictions includes:

[0054] S110. Obtain the inter-satellite spacing representation data used for satellite relative position constraints.

[0055] Among them, the inter-satellite spacing data is determined by the position and motion parameters corresponding to multiple pairs of satellite groups, and the position and motion parameters are determined by the inter-satellite spacing observation and calculation model.

[0056] The satellite group consists of two navigation satellites, and the positional motion parameter corresponding to the satellite group is the positional distance between the three-dimensional coordinate positions of the two navigation satellites.

[0057] In some embodiments, obtaining inter-satellite spacing representation data for satellite relative position constraints includes:

[0058] The coordinate observation data of multiple satellites are obtained from the navigation message of the GNSS receiver; multiple pairs of satellites are formed based on the multiple satellites, and the coordinate observation data corresponding to each pair of satellites is input into the inter-satellite spacing observation calculation model. The position motion parameters corresponding to the multiple pairs of satellites are determined based on the output of the inter-satellite spacing observation calculation model; preset observation noise data is added to the position motion parameters corresponding to the multiple pairs of satellites to obtain the inter-satellite spacing observation data corresponding to each pair of satellites; inter-satellite spacing representation data for constraining the relative position of satellites is constructed using the inter-satellite spacing observation data corresponding to each pair of satellites.

[0059] For example, the coordinate observation data of the i-th satellite is (x i ,y i ,z iThe coordinate observation data of the j-th satellite is (x... j ,y j ,z j The interstellar distance observation calculation model h(x) is represented by the following formula (1).

[0060]

[0061] In formula (1), h ij (x) represents the positional motion parameters corresponding to the satellite group consisting of the i-th satellite and the j-th satellite.

[0062] The inter-satellite spacing observation data corresponding to the satellite group is represented by the following formula (2).

[0063]

[0064] In formula (2), v ij For observation noise; z ij Let be the distance observation values ​​between the i-th and j-th satellites, that is, the inter-satellite spacing observation data corresponding to the satellite group consisting of the i-th and j-th satellites; ||r i -r j ||=h ij (x).

[0065] The inter-satellite spacing data z used for satellite relative position constraints is represented by the following formula (3), which can be used to describe the global observation vector.

[0066]

[0067] S120. Obtain the satellite state vector representation.

[0068] Among them, the satellite state vector representation is used to describe the individual state vector representation corresponding to multiple satellites, and the individual state vector representation is used to describe the position vector and velocity vector of the corresponding satellite.

[0069] In some embodiments, obtaining the satellite state vector representation includes:

[0070] Obtain the position and velocity vectors corresponding to multiple satellites, and determine the state representation data of the satellites using the position and velocity vectors.

[0071] Among them, N satellites are selected as the research objects, and the state of each satellite (i.e., state representation data) is represented by the following formula (4).

[0072]

[0073] In formula (4), x i r represents the state representation data corresponding to the i-th satellite. iLet v be the position vector of the i-th satellite; i Let be the velocity vector of the i-th satellite.

[0074] Construct a single-item state vector representation for the satellite based on the satellite's corresponding state representation data.

[0075] In some embodiments, constructing a satellite-specific state vector representation based on satellite-specific state representation data includes: acquiring preset process noise data for satellite state representation; constructing a satellite dynamics equation representation based on the satellite-specific state representation data; and constructing a satellite-specific state vector representation using the satellite dynamics equation representation and the preset process noise data for satellite state representation.

[0076] Among them, the preset process noise data used for satellite state representation is w k The state transition function is represented by the satellite dynamics equation, which is f(x) k-1 The satellite's corresponding single-term state vector x k It is represented by the following formula (5).

[0077] x k =f(x) k-1 )+w k ,w k ~N(0,Q) (5)

[0078] In formula (5), Q is the process noise covariance.

[0079] A global state transformation is performed on the individual state vector representation corresponding to each satellite to obtain the satellite state vector representation.

[0080] The satellite state vector x is represented by the following formula (6), which can be used to describe a 6N-dimensional global state vector.

[0081]

[0082] S130. Using a preset filtering algorithm, the satellite state vector representation is optimized by using the inter-satellite spacing data used for satellite relative position constraints, and the optimal estimation result of the satellite orbit prediction value is obtained.

[0083] The preset filtering algorithm can be such as Kalman filtering or least squares method, and the optimal estimation result is used to describe the optimal estimated position and optimal estimated velocity of the satellite.

[0084] In some embodiments, a preset filtering algorithm is used to optimize the satellite state vector representation using the inter-satellite spacing data used for satellite relative position constraints, thereby obtaining the optimal estimation result of the satellite orbit prediction value, including:

[0085] State transition prediction is performed on the satellite state vector representation to obtain the corresponding transition state vector representation and covariance vector representation. The position observation parameters corresponding to the transition from the satellite state vector representation to the transition state vector representation are determined through the inter-satellite distance observation calculation model. The observation residual representation is constructed using the inter-satellite distance representation data and the position observation parameters, and the transition state vector representation is updated using the observation residual representation. Finally, the covariance vector representation is updated using the Kalman gain data and the observation matrix representation to obtain the optimal estimation result of the satellite orbit prediction value.

[0086] Among them, the state transition prediction of the satellite state vector representation can be realized by setting the prediction step. The resulting transition state vector representation and covariance vector representation of the satellite state vector representation are shown in the following formula (7).

[0087]

[0088] In formula (7), The state transition Jacobian matrix; P is represented by a state transition vector. k|k-1 It is represented by the covariance vector.

[0089] The position observation parameters corresponding to the transition from the satellite state vector representation to the transition state vector representation are determined using the inter-satellite ... The observation residuals constructed using the interstellar distance data and position observation parameters are represented by the following formula (8).

[0090]

[0091] In formula (8), y k This represents the observed residuals.

[0092] Observation matrix representation H k The result is determined by the position observation parameters and the transition state vector, as shown in the following formula (9).

[0093]

[0094] Kalman gain data K k The following formula (10) represents this.

[0095]

[0096] In formula (10), R k To observe the noise covariance matrix.

[0097] The updated transition state vector representation, which is represented by the observation residual, and the updated covariance vector representation, which is represented by the Kalman gain data and the observation matrix, are shown in Equation (11).

[0098]

[0099] In this embodiment, given the initial conditions r(t0) = r0 and v(t0) = v0, the satellite spacing is used as the observation, and Kalman filtering is applied to perform optimal recursive estimation calculation of the satellite orbit, thereby effectively improving the orbit prediction accuracy.

[0100] In this embodiment, inter-satellite spacing data for constraining satellite relative positions is acquired. This data is determined by the position and motion parameters corresponding to multiple pairs of satellites, which are derived from the inter-satellite spacing observation and calculation model. Satellite state vector representations are also acquired, describing the individual state vector representations of multiple satellites. These individual state vector representations describe the position and velocity vectors of their respective satellites. A preset filtering algorithm is used to optimize the satellite state vector representations using the inter-satellite spacing data for constraining satellite relative positions, resulting in the optimal estimate of the satellite orbit prediction. This optimal estimate describes the optimal estimated position and velocity of the satellite. Thus, by introducing inter-satellite spacing as an orbital dynamic constraint as an observation to constrain the relative positions of satellites, the satellite state vectors are optimized, effectively improving the accuracy of satellite orbit prediction.

[0101] In some embodiments, it also includes:

[0102] Obtain the satellite orbit prediction model; input the satellite state vector representation into the satellite orbit prediction model, determine the predicted values ​​of motion parameters based on the output of the satellite orbit prediction model; optimize the satellite orbit prediction model based on the optimal estimation results of the predicted values ​​of motion parameters and satellite orbit.

[0103] Specifically, the model can be adjusted by calculating the loss value between the optimal estimates of the predicted motion parameters and the predicted satellite orbit. If the loss value is greater than a preset threshold, the satellite orbit prediction model is globally optimized; if the loss value is less than or equal to the preset threshold, the model is locally optimized. This effectively adjusts the satellite orbit prediction model and ensures its timeliness.

[0104] In some embodiments, it also includes:

[0105] Upon receiving a request to acquire satellite orbital motion data, the system determines the target reference value of the satellite orbit based on the optimal estimation result of the satellite orbit prediction value and the predicted values ​​of motion parameters determined by the satellite orbit prediction model. In response to the request to acquire satellite orbital motion data, the system sends the target reference value of the satellite orbit, the optimal estimation result of the satellite orbit prediction value, and the predicted values ​​of motion parameters to the service node equipment.

[0106] Different weights can be assigned to the optimal estimate of the satellite orbit prediction and the predicted motion parameters determined by the satellite orbit prediction model to determine the target reference value of the satellite orbit. For example, the average of the optimal estimate of the satellite orbit prediction and the predicted motion parameters determined by the satellite orbit prediction model can be used as the target reference value of the satellite orbit. Therefore, by sending the target reference value of the satellite orbit, the optimal estimate of the satellite orbit prediction, and the predicted motion parameters to the service node equipment, service personnel can gain a deeper understanding of the satellite orbit prediction situation.

[0107] Figure 2 This is a schematic diagram of the structure of an optimal estimation device for satellite orbit prediction provided in this embodiment. The optimal estimation device for satellite orbit prediction may include: a first acquisition module 210, a second acquisition module 220, and an optimization module 230.

[0108] The first acquisition module 210 is used to acquire inter-satellite spacing representation data for satellite relative position constraints. The inter-satellite spacing representation data is determined by the position motion parameters corresponding to multiple pairs of satellite groups. The position motion parameters are determined by the inter-satellite spacing observation and calculation model.

[0109] The second acquisition module 220 is used to acquire satellite state vector representations, which are used to describe the individual state vector representations corresponding to multiple satellites. The individual state vector representations are used to describe the position vector and velocity vector of the corresponding satellite.

[0110] The optimization module 230 is used to optimize the satellite state vector representation by using a preset filtering algorithm and the inter-satellite spacing representation data used for satellite relative position constraints, so as to obtain the optimal estimation result of the satellite orbit prediction value. The optimal estimation result is used to describe the optimal estimated position and optimal estimated velocity of the satellite.

[0111] The optimal estimation device for satellite orbit prediction provided in this disclosure can execute the above-described method embodiments. For its specific implementation principle and technical effects, please refer to the above-described method embodiments. This disclosure will not repeat them here.

[0112] This application also provides a computer device. Please refer to the following for details. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0113] The computer device includes a memory 310 and a processor 320 that are communicatively connected to each other via a system bus. It should be noted that only a computer device with memory 310 and processor 320 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0114] Computer devices can include desktop computers, laptops, handheld computers, and cloud servers. These devices allow for human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0115] The memory 310 includes at least one type of readable storage medium, including non-volatile memory or volatile memory, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. RAM may include static RAM or dynamic RAM. In some embodiments, the memory 310 may be an internal storage unit of a computer device, such as the hard disk or RAM of the computer device. In other embodiments, the memory 310 may also be an external storage device of the computer device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, or flash card equipped on the computer device. Of course, the memory 310 may include both internal storage units and external storage devices of the computer device. In this embodiment, the memory 310 is typically used to store the operating system and various application software installed on the computer device, such as the program code of the methods described above. Furthermore, the memory 310 may also be used to temporarily store various types of data that have been output or will be output.

[0116] Processor 320 is typically used to perform overall operations of a computer device. In this embodiment, memory 310 is used to store program code or instructions, including computer operation instructions, and processor 320 is used to execute the program code or instructions stored in memory 310 or process data, such as program code that runs the methods described above.

[0117] In this article, the bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus system can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0118] Another embodiment of this application also provides a computer-readable medium, which may be a computer-readable signal medium or a computer-readable medium. A processor in a computer reads computer-readable program code stored in the computer-readable medium, enabling the processor to execute the functional actions specified in each step or combination of steps in the above method; and to generate means for implementing the functional actions specified in each block or combination of blocks in the block diagram.

[0119] Computer-readable media include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared memory or semiconductor systems, devices or apparatuses, or any suitable combination thereof, wherein the memory is used to store program code or instructions, the program code including computer operation instructions, and the processor is used to execute the program code or instructions of the above-described methods stored in the memory.

[0120] The definitions of memory and processor can be found in the description of the foregoing computer device embodiments, and will not be repeated here.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0122] In the various embodiments of this application, the functional units or modules can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0123] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" as described in this application does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims listing several means, several units of these means may be embodied by the same item of hardware. The use of "first," "second," and "third," etc., does not indicate any order and these words should be interpreted as names. Unless otherwise specified, the steps in the above embodiments should not be construed as limiting the order of execution.

[0125] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An optimal estimation method for satellite orbit predictions, characterized in that, include: The inter-satellite spacing data used for satellite relative position constraints is obtained. The inter-satellite spacing data is determined by the position motion parameters corresponding to multiple pairs of satellite groups. The position motion parameters are determined by the inter-satellite spacing observation and calculation model. Obtain satellite state vector representations, which are used to describe individual state vector representations corresponding to multiple satellites, and the individual state vector representations are used to describe the position vector and velocity vector of the corresponding satellite. A preset filtering algorithm is used to optimize the satellite state vector representation using the inter-satellite spacing data used for satellite relative position constraints, thereby obtaining the optimal estimation result of the satellite orbit prediction value. The optimal estimation result is used to describe the optimal estimated position and optimal estimated velocity of the satellite.

2. The method according to claim 1, characterized in that, The acquisition of inter-satellite spacing representation data for satellite relative position constraints includes: Obtain coordinate observation data of multiple satellites from the navigation message of the GNSS receiver; Multiple pairs of satellites are formed by multiple satellites, and the coordinate observation data corresponding to each pair of satellites is input into the inter-satellite spacing observation calculation model. The position and motion parameters corresponding to the multiple pairs of satellites are determined based on the output of the inter-satellite spacing observation calculation model. By adding preset observation noise data to the position motion parameters corresponding to multiple pairs of satellite groups, the inter-satellite spacing observation data corresponding to each pair of satellite groups can be obtained. The inter-satellite spacing representation data, used for constraining the relative positions of satellites, is constructed using the inter-satellite spacing observation data corresponding to each pair of satellite groups.

3. The method according to claim 1, characterized in that, The acquisition of the satellite state vector representation includes: Obtain the position vectors and velocity vectors corresponding to multiple satellites, and determine the state representation data corresponding to the satellites using the position vectors and velocity vectors corresponding to the satellites; Construct a single-item state vector representation of the satellite based on the state representation data corresponding to the satellite; A global state transformation is performed on the individual state vector representation corresponding to each satellite to obtain the satellite state vector representation.

4. The method according to claim 3, characterized in that, The construction of the single-item state vector representation of the satellite based on the state representation data corresponding to the satellite includes: Acquire preset process noise data for satellite status representation; A satellite dynamic equation representation is constructed based on the state representation data corresponding to the satellite; The satellite's state vector representation is constructed using the satellite dynamics equations and the preset process noise data used for satellite state representation.

5. The method according to claim 2, characterized in that, The process employs a preset filtering algorithm to optimize the satellite state vector representation using the inter-satellite spacing data used for satellite relative position constraints, thereby obtaining the optimal estimation result for the satellite orbit prediction value, including: State transition prediction is performed on the satellite state vector representation to obtain the transition state vector representation and covariance vector representation corresponding to the satellite state vector representation; and the position observation parameters corresponding to the transition from the satellite state vector representation to the transition state vector representation are determined by the inter-satellite distance observation calculation model. An observation residual representation is constructed using the interstellar distance representation data and the position observation parameters. The transition state vector representation is updated using the observation residual representation. The covariance vector representation is updated using the Kalman gain data and the observation matrix representation to obtain the optimal estimation result of the satellite orbit prediction. The observation matrix is ​​determined by the position observation parameters and the transition state vector.

6. The method according to claim 1, characterized in that, Also includes: Obtain satellite orbit prediction models; The satellite state vector representation is input into the satellite orbit prediction model, and the predicted values ​​of motion parameters are determined based on the output of the satellite orbit prediction model. Based on the optimal estimation results of the predicted motion parameters and the predicted satellite orbit, the satellite orbit prediction model is optimized.

7. The method according to claim 6, characterized in that, Also includes: Receive a request to acquire satellite orbit motion data, and determine a satellite orbit target reference value by using the optimal estimation result of the satellite orbit prediction value and the predicted value of the motion parameters determined by the satellite orbit prediction model; In response to the request to acquire the satellite orbit motion data, the system sends the satellite orbit target reference value, the optimal estimation result of the satellite orbit prediction value, and the predicted value of the motion parameters to the service node device.

8. The method according to claim 1, characterized in that, The satellite group consists of two navigation satellites, and the positional motion parameter corresponding to the satellite group is the positional distance between the three-dimensional coordinate positions of the two navigation satellites.

9. The method according to claim 3, characterized in that, The satellite state vector representation is used to describe a 6N-dimensional global state vector.

10. The method according to claim 1, characterized in that, The preset filtering algorithm is either the Kalman filter algorithm or the least squares method.

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