Method and device for state estimation and error identification of autonomous navigation system

Abstract

The invention provides a state estimation and error identification method and device for an autonomous navigation system, and the method comprises the following steps: obtaining an observability evaluation index of the system by using an observability criterion; according to the observability evaluation index of the system, obtaining the accurate compensation degree of the measurement error of the optical sensor, and determining the achievable state estimation accuracy of the autonomous navigation system; constructing an observability optimization function; analyzing a coupling relationship between a measurement error and a system state based on an observability optimization function, and establishing an error response model; according to the error response model, constructing a system state parameter joint optimization model through the observability evaluation index of the navigation system; and optimizing the system state parameter joint optimization model and realizing on-orbit identification of errors and system states. The accuracy of observability judgment of the navigation system is improved, observation information and calculation resource allocation can be autonomously optimized, and the problem of state estimation under the weak observation condition is solved.

Description

Technical Field

[0001] This invention relates to the field of on-orbit measurement error technology, and specifically to a method and apparatus for state estimation and error identification of an autonomous navigation system. Background Technology

[0002] The ultimate goal of deep space exploration optical autonomous navigation is to autonomously acquire high-precision navigation parameters such as the absolute and relative position, velocity, and motion state between the probe and the target. These parameters are a prerequisite for realizing deep space exploration missions and need to be obtained through accurate identification of on-orbit errors and autonomous navigation filtering algorithms.

[0003] The severely limited onboard computing resources of deep space probes significantly restrict the on-orbit identification of errors and the autonomous operation capabilities of autonomous navigation algorithms. For example, Japan's Hayabusa2 probe, which successfully returned asteroid samples in 2020, had a COSMO16 processor with a clock speed of only 40MHz and a memory and cache size of only 16Mb, far below the computing and storage capabilities of ground-based computers. Furthermore, the optical autonomous navigation process, in addition to achieving complete estimation of the navigation system's state, also requires on-orbit identification of the comprehensive error model / parameters of the optical sensors, greatly increasing the computational complexity of the optical autonomous navigation algorithm and affecting the identification of system errors and the convergence of the navigation algorithm.

[0004] Domestic and international researchers have made some progress in on-orbit accurate compensation for navigation system errors and the design of autonomous navigation filtering algorithms. However, most system error compensation methods rely on redundant measurement information, which is not suitable for deep space exploration optical autonomous navigation systems with limited optical measurement information. System error compensation and navigation filtering algorithms usually improve autonomous navigation accuracy at the cost of increased computational complexity. The algorithm process is complex and difficult to operate autonomously on-board under the constraints of limited onboard computing resources.

[0005] In summary, the existing technology has the following problems: how to design an efficient and high-precision autonomous navigation algorithm to achieve a complete estimation of navigation status under the constraint of severely limited spaceborne resources and in conjunction with an observability evaluation system. Summary of the Invention

[0006] The purpose of this invention is to solve the problem of how to design an efficient and high-precision autonomous navigation algorithm to achieve complete estimation of navigation status under the constraint of severely limited spaceborne resources and in conjunction with an observability evaluation system.

[0007] To this end, in one aspect, embodiments of the present invention provide a method for state estimation and error identification of an autonomous navigation system, the method comprising the following steps:

[0008] Utilize observability criteria to obtain evaluation indicators for system observability;

[0009] Based on the system's observability evaluation index, the degree of accurate compensation for optical sensor measurement errors is obtained, and the achievable state estimation accuracy of the autonomous navigation system is determined.

[0010] Construct an observability optimization function based on the state estimation accuracy achievable by the autonomous navigation system;

[0011] Based on the aforementioned observability optimization function, the coupling relationship between measurement error and system state is analyzed, and an error response model is established.

[0012] Based on the error response model, a joint optimization model for system state parameters is constructed using the navigation system observability evaluation index.

[0013] The joint optimization model of the system state parameters is optimized to achieve on-orbit identification of errors and system state.

[0014] On the other hand, embodiments of the present invention provide an autonomous navigation system state estimation and error identification device, comprising:

[0015] The acquisition unit is used to obtain the system's observability evaluation index using observability criteria;

[0016] The computing unit is used to obtain the degree of accurate compensation for the measurement error of the optical sensor based on the system's observability evaluation index, and to determine the state estimation accuracy that the autonomous navigation system can achieve.

[0017] The optimization unit is used to construct an observability optimization function based on the state estimation accuracy achievable by the autonomous navigation system.

[0018] The response unit is used to analyze the coupling relationship between measurement error and system state based on the observability optimization function and to establish an error response model.

[0019] The construction unit is used to construct a joint optimization model of system state parameters based on the error response model and the navigation system observability evaluation index.

[0020] The identification unit is used to optimize the joint optimization model of the system state parameters and realize the on-orbit identification of errors and system state.

[0021] The above technical solution has the following beneficial effects:

[0022] This invention addresses the problems of incomplete on-orbit optical measurement information and limited onboard processing resources. It analyzes the impact mechanism of system observability on system navigation parameter / state estimation, explores a method to improve system observability based on deep space probe orbit and attitude maneuvers, and adopts an adaptive filtering approach that minimizes the impact of uncertain model errors and non-Gaussian noise on navigation filtering performance, based on the quantitative description and evaluation system of system observability. It designs the optimal index for navigation filtering, optimizes the navigation filtering process and filtering structure, and resolves the inherent contradiction between weak, under-observable, or unobservable systems and complete estimation of system navigation state and accurate error compensation. It obtains complete estimation of navigation state and accurate on-orbit identification of system errors under weak observation conditions.

[0023] It improves the accuracy of observability determination of navigation systems, enables autonomous optimization of the allocation of observation information and computing resources, and solves the problem of state estimation under weak observation conditions;

[0024] The optical sensor error model was simplified, the on-board data processing burden was reduced, and accurate identification and compensation of multi-source errors were achieved.

[0025] The navigation algorithm has low computational complexity and good convergence. Even under the condition of limited onboard computing resources, it can still guarantee high accuracy of navigation state estimation. The root mean square error of position estimation is more than 15% better than existing methods, and the computation time is reduced by more than 20%.

[0026] It is applicable to all stages of deep space exploration missions and can be directly applied to various deep space exploration missions such as asteroid exploration and Mars landing, providing theoretical and technical support for the design of my country's autonomous navigation system for deep space exploration. Attached Figure Description

[0027] Figure 1 This is a flowchart of a method for state estimation and error identification of an autonomous navigation system provided in an embodiment of the present invention;

[0028] Figure 2 This is a schematic diagram of the structure of an autonomous navigation system state estimation and error identification device provided in an embodiment of the present invention;

[0029] Figure 3 This is a flowchart of the first implementation method of an autonomous navigation system state estimation and error identification method provided by the present invention;

[0030] Figure 4 This is a flowchart of the second implementation of an autonomous navigation system state estimation and error identification method provided in this invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] The core of this invention is the on-board autonomous and complete estimation of navigation status and parameters, and the on-orbit autonomous and accurate identification of errors. This is also a prerequisite for realizing subsequent deep space exploration missions. Addressing the constraints of severely limited onboard resources, limited measurement information, and multiple sources of error during the on-orbit operation of deep space probes, this invention explores two approaches: firstly, it starts with the observability evaluation system of the navigation system to investigate methods for improving and optimizing the system's observability; secondly, it simplifies the model of the parameters to be estimated, designs efficient navigation filtering algorithms, and improves the efficiency of navigation system state estimation and parameter identification.

[0033] This invention, based on system observability theory, multi-source error reduction modeling, and adaptive filtering algorithms, forms a complete technical chain encompassing observability optimization, error modeling reduction, state estimation, and error identification. The specific steps are as follows:

[0034] In embodiments of the present invention, such as Figure 1 This paper provides a method for state estimation and error identification of an autonomous navigation system, the method comprising the following steps:

[0035] S101: Using observability criteria, obtain evaluation indicators for the observability of the system; including:

[0036] Based on the on-orbit operation phase of the probe, determine the characteristics and dimensional information of the measurement data obtained by the current system;

[0037] By combining the comprehensive error reduction model of the optical sensor and using the observability criterion, it is determined whether the computing resources and measurement information of the navigation system can meet the performance indicators of the autonomous navigation system;

[0038] It also determines the priority of the constraints of computational resources and measurement information on the state estimation and error accuracy compensation of the autonomous navigation system, providing a basis for evaluating the system's observability.

[0039] First, based on the on-orbit operation phase of the detector, the characteristics and dimensions of the measurement data obtained by the current system are determined. Combined with the comprehensive error reduction model of the optical sensor, the observability criterion is used to determine whether the computing resources and measurement information of the navigation system can meet the performance indicators of the autonomous navigation system. The priority of the constraints of computing resources and measurement information on the state estimation and error accurate compensation of the autonomous navigation system is also determined, providing direction for the evaluation of the system's observability.

[0040] This study, combining an autonomous evaluation system for the observability of navigation systems with optimization theory, aims to increase the sources of autonomous navigation information by optimizing the orbital and attitude maneuvers of deep space probes and improving observation information. It also seeks to reduce the computational complexity of navigation filtering and error identification algorithms by optimizing navigation algorithm structure and computational resources. The research focuses on information acquisition and resource allocation methods to enhance the observability of navigation systems. On the one hand, it effectively improves the observability of navigation systems under conditions of weak or under-observation, achieving the required navigation accuracy / efficiency. On the other hand, under permissible measurement conditions, it optimizes the selection of navigation system observation information and the allocation of computational resources to maximize the accuracy and efficiency of autonomous navigation.

[0041] S102: Based on the system observability evaluation index, obtain the degree of accurate compensation for optical sensor measurement errors, and determine the achievable state estimation accuracy of the autonomous navigation system; including:

[0042] Based on the results of the system observability criterion and combined with the observability evaluation system, the computational resource consumption and measurement data information volume are obtained.

[0043] The extent to which quantifiable observability affects the system's navigation accuracy;

[0044] To obtain the precise degree of compensation for measurement errors of the optical sensor;

[0045] Determine the achievable state estimation accuracy of the autonomous navigation system;

[0046] To establish the quantitative basis required for optimizing the observability of the system.

[0047] Based on the results of the system observability criterion and combined with the observability capability evaluation system, the computational resource consumption and measurement data information are determined, the impact of the current observability of the system on the system navigation accuracy is quantified, the degree of accurate compensation for optical sensor measurement error is obtained, and the state estimation accuracy that the autonomous navigation system can achieve is determined, thus providing a quantitative basis for the optimization of the system observability.

[0048] S103: Construct an observability optimization function based on the state estimation accuracy achievable by the autonomous navigation system;

[0049] like Figure 3As shown, when measurement information is sufficient but computational resources are insufficient, an observability optimization function is constructed by integrating the observability criteria and quantitative indicators of the integrated navigation system. Optimization theory methods are then used to optimize the selection of measurement information and rationally allocate computational resources. When measurement is insufficient, methods such as active orbital maneuvers and attitude maneuvers of the probe are combined to increase known information. Optimization theory is then used to solve for the optimal probe maneuvering mode, addressing the underdetermined problems of some error parameters and state variables, thereby improving the system's observability.

[0050] S104: Based on the aforementioned observability optimization function, analyze the coupling relationship between measurement error and system state, and establish an error response model;

[0051] Based on the system observability characterization model, the coupling relationship between optical sensor measurement error and system state variables is analyzed, the influence mechanism on the accuracy of navigation system state estimation is explored, the analytical relationship between the observability of autonomous navigation system error compensation and system state variables is constructed, and an error response model of optical autonomous navigation system is established.

[0052] S105: Based on the aforementioned error response model, construct a joint optimization model for system state parameters using the navigation system observability evaluation index; including:

[0053] Based on the error response model of the autonomous navigation system, the observability of the navigation system is optimized through the observability evaluation system of the navigation system, and a low-frequency error model is constructed.

[0054] Optimize the sensitive error model by using the optimized sensitive error model parameters and system state variables as parameters to be estimated.

[0055] By combining the parameters to be estimated with a multi-source error integrated characterization model, a joint optimization model for system state parameters is established.

[0056] Specifically, based on the error response model of the autonomous navigation system, the observability of the navigation system is optimized through the observability evaluation system of the navigation system. Sensitive error models such as low-frequency error model, measurement reference error model and rotational installation error model are constructed. The parameters of the optimized sensitive error model and the system state variables are used as parameters to be estimated. Combined with the multi-source error integrated characterization model, a joint optimization model of system state parameters is established, which effectively reduces the problem of insufficient observability of the navigation system caused by too many error parameters.

[0057] S106: Optimize the joint optimization model of the system state parameters and achieve on-orbit identification of errors and system states. This includes:

[0058] Based on the hierarchical analysis results of the measurement error of the optical sensor, a mapping relationship is established between the error continuity, sparsity, and stability characteristics and the multi-source error characteristic subspace of the autonomous navigation system.

[0059] Different types of error parameters are mapped to existing model parameters to optimize the joint optimization model of system state parameters;

[0060] By combining autonomous navigation state estimation algorithms, accurate on-orbit estimation and compensation of errors can be achieved, enabling on-orbit identification of errors and system states.

[0061] As one implementation method: Figure 4 As shown, based on the hierarchical analysis results of optical sensor measurement errors, a mapping relationship is established between the characteristics of error continuity, sparsity, and stability and the multi-source error feature subspace of the autonomous navigation system. Different types of error parameters are mapped to existing model parameters, further improving the joint optimization model of system state parameters. Combined with the autonomous navigation state estimation algorithm, accurate on-orbit estimation and compensation of errors are achieved. On the one hand, this minimizes the impact of navigation system errors on navigation accuracy, and on the other hand, it enables on-orbit identification of errors and system states.

[0062] In embodiments of the present invention, such as Figure 2 Furthermore, an autonomous navigation system state estimation and error identification device is provided, comprising:

[0063] The acquisition unit 21 is used to obtain the system's observability evaluation index using the observability criterion;

[0064] The calculation unit 22 is used to obtain the degree of accurate compensation for the measurement error of the optical sensor based on the system's observability evaluation index, and to determine the state estimation accuracy that the autonomous navigation system can achieve.

[0065] Optimization unit 23 is used to construct an observability optimization function based on the state estimation accuracy achievable by the autonomous navigation system;

[0066] The response unit 24 is used to analyze the coupling relationship between measurement error and system state based on the observability optimization function and establish an error response model;

[0067] Construction unit 25 is used to construct a joint optimization model of system state parameters based on the error response model and the navigation system observability evaluation index;

[0068] The identification unit 26 is used to optimize the joint optimization model of the system state parameters and realize the on-orbit identification of errors and system state.

[0069] The acquisition unit 21 includes:

[0070] Used to determine the characteristics and dimensional information of the measurement data obtained by the current system based on the on-orbit operation phase of the detector;

[0071] It is used to combine the comprehensive error reduction model of optical sensor and use the observability criterion to determine whether the computing resources and measurement information of the navigation system can meet the performance indicators of the autonomous navigation system;

[0072] It is used to determine the priority of constraints on the state estimation and error accuracy compensation of autonomous navigation systems by computing resources and measurement information, and to provide a basis for evaluating the observability of the system.

[0073] The computing unit 22 includes:

[0074] It is used to obtain the amount of computing resources used and the amount of measurement data information based on the results of the system observability criterion and in combination with the observability evaluation system;

[0075] Used to quantify the impact of observability on the system's navigation accuracy;

[0076] Used to obtain the precise degree of compensation for measurement errors of optical sensors;

[0077] Used to determine the achievable state estimation accuracy of an autonomous navigation system;

[0078] Used to form the quantitative basis needed to optimize the observability of the system.

[0079] The building unit 25 includes:

[0080] This is used to optimize the observability of the navigation system based on the error response model of the autonomous navigation system and to construct a low-frequency error model through the evaluation system of the observability of the navigation system.

[0081] To optimize the sensitive error model, the optimized sensitive error model parameters and system state variables are used as parameters to be estimated.

[0082] This is used to combine the parameters to be estimated with a multi-source error integrated characterization model to establish a joint optimization model for system state parameters.

[0083] The identification unit 26 includes:

[0084] This is used to establish a mapping relationship between the error continuity, sparsity, and stability characteristics and the multi-source error characteristic subspace of the autonomous navigation system based on the hierarchical analysis results of the measurement error of the optical sensor.

[0085] Used to map different types of error parameters to existing model parameters, and to optimize the joint optimization model of system state parameters;

[0086] It is used to combine autonomous navigation state estimation algorithms to achieve accurate on-orbit error estimation and compensation, and to achieve on-orbit identification of errors and system states.

[0087] An autonomous navigation system state estimation and error identification device adopts the above-mentioned autonomous navigation system state estimation and error identification method. Its principle and process are the same as those of the autonomous navigation system state estimation and error identification method, and will not be repeated here.

[0088] Example:

[0089] This invention provides a method for state estimation and error identification of an autonomous navigation system. Specifically, the core of this invention is the on-board autonomous and complete estimation of navigation state and parameters, and the on-orbit autonomous and accurate identification of errors, which are also prerequisites for realizing subsequent deep space exploration missions. The optical information of deep space exploration optical autonomous navigation systems is incomplete, and on-board computing resources are severely limited. These constraints determine that optical autonomous navigation systems are typical weak / under-observed systems, which greatly restricts the autonomy, accuracy, and effectiveness of state estimation and error identification. Therefore, it is necessary to combine the conditions of weak / under-observed systems with a qualitative and quantitative evaluation system of observability, further study methods to improve and optimize the observability of navigation systems, and optimize the navigation filtering algorithm structure by using methods such as nonlinear identification, adaptive filtering, performance analysis, and evaluation, addressing the nonlinearity and uncertainty of the optical autonomous navigation system model and parameters, and the non-Gaussianity of noise. This includes researching algorithms for accurate identification of optical measurement errors and complete estimation of navigation state. Specifically, this includes:

[0090] 1) Optimization and Enhancement of Navigation System Observability: Combining the autonomous evaluation system of navigation system observability, and utilizing optimization theory, this study aims to increase the sources of autonomous navigation information by optimizing the orbital maneuvers, attitude maneuvers, and observation information of deep space probes. It also aims to reduce the computational complexity of navigation filtering and error identification algorithms by optimizing the navigation algorithm structure and computational resources. The study investigates methods for information acquisition and resource allocation to enhance the observability of the navigation system. On the one hand, under conditions of weak / under-observation, it aims to effectively improve the observability of the navigation system to meet the required navigation accuracy / efficiency. On the other hand, under conditions where the measurement conditions of the navigation system permit, it aims to achieve optimized selection of navigation system observation information and optimized allocation of computational resources to maximize the accuracy / efficiency of autonomous navigation.

[0091] 2) Joint Optimization Model of Navigation State Parameters and Accurate On-Orbit Error Compensation: Explore the intrinsic law between the observability of on-orbit compensation of system errors and the system state, and study the influence mechanism of measurement errors on the improvement of autonomous navigation accuracy; combining the integrated characterization of on-orbit measurement errors of optical sensors and the constructed comprehensive reduced error model, starting from the accuracy requirements of autonomous navigation systems, and based on optimizing the observability of navigation systems, construct an autonomous navigation joint optimization model with key error model parameters and navigation state as parameters to be estimated; incorporate constraints such as sparsity, smoothness, and continuity of key error model parameters and manifold structure into the joint optimization model to achieve accurate on-orbit compensation of measurement errors while performing navigation filtering.

[0092] 3) Optimization Design of High-Efficiency and High-Precision Adaptive Navigation Wave Algorithm: Based on the research on the applicability of nonlinear filtering algorithms for general dynamic systems and the analysis of dynamic system filtering performance, this paper addresses the new problems brought about by the nonlinearity and uncertainty of autonomous navigation models / parameters, and the non-Gaussianity of noise to the design of navigation filtering algorithms. It analyzes the impact of nonlinearity, uncertainty, and non-Gaussianity on autonomous navigation performance. Under the condition of limited onboard resources, combined with the quantitative evaluation system of navigation system observability, an adaptive filtering approach is adopted to minimize the impact of uncertain model errors and non-Gaussian noise on navigation filtering performance. The optimal index for navigation filtering is designed, and the navigation filtering process and structure are optimized to obtain a complete estimation of navigation state and accurate on-orbit identification of system errors under weak / under-observation conditions. The complexity, computational efficiency, accuracy, and reliability of the optimized navigation filtering algorithm are theoretically analyzed to improve the efficiency and accuracy of the autonomous navigation algorithm.

[0093] This invention addresses the problems of incomplete on-orbit optical measurement information and limited onboard processing resources. It analyzes the impact mechanism of system observability on system navigation parameter / state estimation, explores a method to improve system observability based on deep space probe orbit and attitude maneuvers, and adopts an adaptive filtering approach that minimizes the impact of uncertain model errors and non-Gaussian noise on navigation filtering performance, based on the quantitative description and evaluation system of system observability. It designs the optimal index for navigation filtering, optimizes the navigation filtering process and filtering structure, and resolves the inherent contradiction between weak, under-observable, or unobservable systems and complete estimation of system navigation state and accurate error compensation. It obtains complete estimation of navigation state and accurate on-orbit identification of system errors under weak observation conditions.

[0094] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0095] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for state estimation and error identification of an autonomous navigation system, characterized in that, The method includes the following steps: Utilize observability criteria to obtain evaluation indicators for system observability; Based on the system's observability evaluation index, the degree of accurate compensation for optical sensor measurement errors is obtained, and the achievable state estimation accuracy of the autonomous navigation system is determined. Construct an observability optimization function based on the state estimation accuracy achievable by the autonomous navigation system; Based on the aforementioned observability optimization function, the coupling relationship between measurement error and system state is analyzed, and an error response model is established. Based on the error response model, a joint optimization model for system state parameters is constructed using the navigation system observability evaluation index. The joint optimization model of the system state parameters is optimized to achieve on-orbit identification of errors and system state.

2. The method for state estimation and error identification of an autonomous navigation system according to claim 1, characterized in that, The method of obtaining system observability evaluation indicators using observability criteria includes: Based on the on-orbit operation phase of the probe, determine the characteristics and dimensional information of the measurement data obtained by the current system; By combining the comprehensive error reduction model of the optical sensor and using the observability criterion, it is determined whether the computing resources and measurement information of the navigation system can meet the performance indicators of the autonomous navigation system. It also determines the priority of the constraints of computing resources and measurement information on the state estimation and error accuracy compensation of the autonomous navigation system, providing a basis for evaluating the system's observability.

3. The method for state estimation and error identification of an autonomous navigation system according to claim 1, characterized in that, The step of obtaining the degree of accurate compensation for optical sensor measurement errors based on the system's observability evaluation index, and determining the achievable state estimation accuracy of the autonomous navigation system, includes: Based on the results of the system observability criterion and combined with the observability evaluation system, the computational resource consumption and measurement data information volume are obtained. The extent to which quantifiable observability affects the system's navigation accuracy; To obtain the precise degree of compensation for measurement errors of the optical sensor; Determine the achievable state estimation accuracy of the autonomous navigation system; To establish the quantitative basis required for optimizing the observability of the system.

4. The method for state estimation and error identification of an autonomous navigation system according to claim 1, characterized in that, The step of constructing a joint optimization model for system state parameters based on the error response model and using the navigation system observability evaluation index includes: Based on the error response model of the autonomous navigation system, the observability of the navigation system is optimized through the observability evaluation system of the navigation system, and a low-frequency error model is constructed. Optimize the sensitive error model by using the optimized sensitive error model parameters and system state variables as parameters to be estimated. By combining the parameters to be estimated with a multi-source error integrated characterization model, a joint optimization model for system state parameters is established.

5. The method for state estimation and error identification of an autonomous navigation system according to claim 4, characterized in that, The optimization of the joint optimization model for the system state parameters and the on-orbit identification of errors and system states include: Based on the hierarchical analysis results of the measurement error of the optical sensor, a mapping relationship is established between the error continuity, sparsity, stability characteristics and the multi-source error characteristic subspace of the autonomous navigation system. Different types of error parameters are mapped to existing model parameters to optimize the joint optimization model of system state parameters; By combining autonomous navigation state estimation algorithms, accurate on-orbit estimation and compensation of errors can be achieved, enabling on-orbit identification of errors and system states.

6. A state estimation and error identification device for an autonomous navigation system, characterized in that, include: The acquisition unit is used to obtain the system's observability evaluation index using observability criteria; The computing unit is used to obtain the degree of accurate compensation for the measurement error of the optical sensor based on the system's observability evaluation index, and to determine the state estimation accuracy that the autonomous navigation system can achieve. The optimization unit is used to construct an observability optimization function based on the state estimation accuracy achievable by the autonomous navigation system. The response unit is used to analyze the coupling relationship between measurement error and system state based on the observability optimization function and to establish an error response model. The construction unit is used to construct a joint optimization model of system state parameters based on the error response model and the navigation system observability evaluation index. The identification unit is used to optimize the joint optimization model of the system state parameters and realize the on-orbit identification of errors and system state.

7. The autonomous navigation system state estimation and error identification device according to claim 6, characterized in that, The acquisition unit includes: Used to determine the characteristics and dimensional information of the measurement data obtained by the current system based on the on-orbit operation phase of the detector; It is used to combine the comprehensive error reduction model of optical sensor and use the observability criterion to determine whether the computing resources and measurement information of the navigation system can meet the performance indicators of the autonomous navigation system; It is used to determine the priority of constraints on the state estimation and error accuracy compensation of autonomous navigation systems by computing resources and measurement information, and to provide a basis for evaluating the observability of the system.

8. The autonomous navigation system state estimation and error identification device according to claim 6, characterized in that, The computing unit includes: It is used to obtain the amount of computing resources used and the amount of measurement data information based on the results of the system observability criterion and in combination with the observability evaluation system; Used to quantify the impact of observability on the system's navigation accuracy; Used to obtain the precise degree of compensation for measurement errors of optical sensors; Used to determine the achievable state estimation accuracy of an autonomous navigation system; Used to form the quantitative basis needed to optimize the observability of the system.

9. The autonomous navigation system state estimation and error identification device according to claim 6, characterized in that, The building unit includes: This is used to optimize the observability of the navigation system based on the error response model of the autonomous navigation system and to construct a low-frequency error model through the evaluation system of the observability of the navigation system. To optimize the sensitive error model, the optimized sensitive error model parameters and system state variables are used as parameters to be estimated. This is used to combine the parameters to be estimated with a multi-source error integrated characterization model to establish a joint optimization model for system state parameters.

10. The autonomous navigation system state estimation and error identification device according to claim 9, characterized in that, The identification unit includes: This is used to establish a mapping relationship between the error continuity, sparsity, and stability characteristics and the multi-source error characteristic subspace of the autonomous navigation system based on the hierarchical analysis results of the measurement error of the optical sensor. Used to map different types of error parameters to existing model parameters, and to optimize the joint optimization model of system state parameters; It is used to combine autonomous navigation state estimation algorithms to achieve accurate on-orbit error estimation and compensation, and to achieve on-orbit identification of errors and system states.

Images