Satellite inertial installation error calibration method and system based on iterative filtering

Abstract

The invention provides a satellite inertial installation error calibration method and system based on iterative filtering, and aims to solve the problem that the attitude measurement precision of a star sensor is influenced by an installation error between inertial navigation and the star sensor. The method comprises the following steps: constructing a state space model and a measurement model of iterative extended Kalman filtering through a star vector under a star sensor body coordinate system, an ephemeris vector under an inertial coordinate system and inertial information, and deducing and calculating to obtain a measurement Jacobian matrix; and substituting the measured Jacobian matrix into the measurement model of the iterative extended Kalman filter to iteratively estimate the satellite inertial installation error, namely the installation error of the inertial astronomical integrated navigation system, so that the function of calibrating the installation error of the inertial astronomical integrated navigation system is realized. The method eliminates the influence of the installation error between the inertial navigation system and the star sensor on the attitude measurement of the star sensor, is suitable for the application occasion of combined navigation, and is good in adaptability and high in precision.

Description

Technical Field

[0001] The invention belongs to the field of surveying and mapping technology, and in particular relates to a satellite inertial installation error calibration method and system based on iterative filtering. Background Art

[0002] Inertial navigation system (INS) is widely used due to its all-day autonomous navigation advantage, but its navigation error diverges over time. Star sensor (STAR) is an important component of astronomical navigation system as a high-precision attitude measurement optical sensor. It measures the vector pointing of stars in the star sensor coordinate system and can provide high-precision attitude reference information for the carrier's attitude control and astronomical navigation system. However, the attitude measurement of star sensor is affected by weather and other observation conditions, and it cannot output attitude continuously. Therefore, the combination of inertial navigation system and star sensor can realize the complementary advantages of the two. The difference between the attitude information provided by the star sensor and the attitude information obtained by inertial navigation solution is used as measurement information. Kalman filtering is used to estimate the attitude misalignment angle of inertial navigation solution and correct it to construct an inertial astronomical combined navigation system, which effectively improves the attitude positioning accuracy of the navigation system.

[0003] However, when the equipment is installed, the installation error between the star sensor and the inertial navigation will affect the attitude accuracy provided by the star sensor. Therefore, in order to eliminate the influence of the installation error on the star sensor attitude measurement, it is necessary to accurately calibrate the installation error between the inertial navigation system and the star sensor, that is, to calibrate the installation error of the inertial astronomical integrated navigation system. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for calibrating a satellite-inertial installation error based on iterative filtering, which are used to calibrate the installation error of an inertial celestial integrated navigation system.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a satellite inertial installation error calibration method based on iterative filtering, comprising the following steps: S0: Obtain the star vector in the star sensor body coordinate system through the star sensor, obtain the corresponding ephemeris star vector in the inertial coordinate system, and obtain inertial information through the inertial navigation system; S1: Construct the state space model and measurement model of iterative extended Kalman filter based on the star vector in the star sensor body coordinate system, the ephemeris star vector in the inertial coordinate system and the inertial information; S2: Based on the state space model and measurement model of iterative extended Kalman filter, the measurement Jacobian matrix is ​​derived and calculated; S3: Substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter to iteratively estimate the installation error of the inertial astronomical integrated navigation system.

[0006] According to the above scheme, in step S1, the specific steps are: S11: Select the three-dimensional installation error angle between the star sensor and the inertial navigation system as the system state variable of the input model; the system state variable at the current moment includes the product of the system state variable at the previous moment and the system state transfer matrix; the system state transfer matrix is ​​a third-order unit matrix; S12: The measurement information output by the model at a certain moment is a nonlinear vector function of the system state variables, which is specifically expressed as the difference between the star vector in the star sensor body coordinate system and the star vector in the inertial navigation system coordinate system.

[0007] Furthermore, in the step S11, the three-dimensional installation error angle includes a pitch direction installation error angle, a roll direction installation error angle and a heading direction installation error angle.

[0008] Furthermore, in step S12, the star vector in the inertial navigation system coordinate system is the product of the attitude matrix obtained by pure inertial navigation, the matrix of the earth coordinate system represented by the local longitude and latitude relative to the geographic coordinate system, the matrix of the inertial system represented by Greenwich sidereal time relative to the earth system, and the ephemeris star vector in the inertial coordinate system corresponding to the star sensor body coordinate system.

[0009] Furthermore, in step S12, the product is expressed as the product of the attitude matrix of the star sensor body coordinate system relative to the inertial navigation system coordinate system and the star vector in the star sensor body coordinate system; the attitude matrix of the star sensor body coordinate system relative to the inertial navigation system coordinate system is an attitude matrix represented by a three-dimensional installation error angle, i.e., a system state variable.

[0010] Furthermore, in the step S1, The system state variables also include system noise; the system noise is the product of the system noise distribution matrix and the system noise vector; The measurement information of the system at a certain moment also includes measurement noise; Both the system noise and the measurement noise are zero-mean Gaussian white noise.

[0011] According to the above scheme, in step S2, the specific steps are: S21: obtaining a one-step prediction of the state in the iterative extended Kalman filter according to the system state variables; S22: Substitute the one-step state prediction in the iterative extended Kalman filter into the nonlinear vector function and derive the measurement Jacobian matrix.

[0012] According to the above scheme, in step S3, the specific steps are: S31: Substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter, and perform pre-filtering according to the ordinary extended Kalman filter method; S32: Perform iterative filtering to complete the calibration of the installation error of the inertial astronomical integrated navigation system.

[0013] A satellite inertial installation error calibration system based on iterative filtering. The data acquisition submodule is used to obtain the star vector in the star sensor body coordinate system through the star sensor, obtain the corresponding ephemeris star vector in the inertial coordinate system, and obtain inertial information through the inertial navigation system; The model building submodule is used to build the state space model and measurement model of iterative extended Kalman filter according to the star vector in the star sensor body coordinate system, the ephemeris star vector in the inertial coordinate system and the inertial information; The matrix calculation submodule is used to derive and calculate the measurement Jacobian matrix based on the state space model and measurement model of iterative extended Kalman filtering; The error estimation submodule is used to substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter to iteratively estimate the installation error of the inertial astronomical integrated navigation system.

[0014] A computer memory stores a computer program executable by a computer processor, wherein the computer program executes a satellite inertial installation error calibration method based on iterative filtering.

[0015] The beneficial effects of the present invention are: 1. A method and system for calibrating the installation error of an inertial navigation system based on iterative filtering of the present invention aims at the problem that the installation error between an inertial navigation system and a star sensor affects the accuracy of star-sensing attitude measurement. A state space model and a measurement model of an iterative extended Kalman filter are constructed through star vectors in a star sensor body coordinate system, ephemeris star vectors in an inertial coordinate system and inertial information. A measurement Jacobian matrix is ​​derived and calculated, and the measurement Jacobian matrix is ​​substituted into the measurement model of the iterative extended Kalman filter to iteratively estimate the installation error of an inertial astronomical integrated navigation system, thereby realizing the function of calibrating the installation error of an inertial astronomical integrated navigation system.

[0016] 2. The present invention eliminates the influence of the installation error between the inertial navigation system and the star sensor on the star sensor attitude measurement, is suitable for the application occasions of combined navigation, has good adaptability and high precision.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a flow chart of an embodiment of the present invention.

[0020] Figure 2 4 is a flowchart of iterative filtering according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] Example 1 See also Figure 1 , the specific steps of a satellite inertial installation error calibration method based on iterative filtering are as follows: S0: Obtain the star vector in the star sensor body coordinate system through the star sensor, obtain the corresponding ephemeris star vector in the inertial coordinate system, and obtain inertial information through the inertial navigation system; S1: Construct the state space model and measurement model of iterative extended Kalman filter based on the star vector in the star sensor body coordinate system, the ephemeris star vector in the inertial coordinate system and the inertial information; S2: Based on the state space model and measurement model of iterative extended Kalman filter, the measurement Jacobian matrix is ​​derived and calculated; S3: Substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter to iteratively estimate the installation error of the inertial astronomical integrated navigation system.

[0023] Furthermore, in step S1, the specific steps are: S11: Select the three-dimensional installation error angle between the star sensor and the inertial navigation system as the system state variable of the input model; the system state variable at the current moment includes the product of the system state variable at the previous moment and the system state transfer matrix; the system state transfer matrix is ​​a third-order unit matrix; S12: The measurement information output by the model at a certain moment is a nonlinear vector function of the system state variables, which is specifically expressed as the difference between the star vector in the star sensor body coordinate system and the star vector in the inertial navigation system coordinate system.

[0024] Further, in step S11, the three-dimensional installation error angle includes a pitch direction installation error angle, a roll direction installation error angle and a heading direction installation error angle.

[0025] Further, in step S12, the star vector in the inertial navigation system coordinate system is the product of the attitude matrix obtained by pure inertial navigation, the matrix of the earth coordinate system represented by the local longitude and latitude relative to the geographic coordinate system, the matrix of the inertial system represented by Greenwich sidereal time relative to the earth system, and the ephemeris star vector in the inertial coordinate system corresponding to the star sensor body coordinate system.

[0026] Further, in step S12, the product is expressed as the product of the attitude matrix of the star sensor body coordinate system relative to the inertial navigation system coordinate system and the star vector in the star sensor body coordinate system; the attitude matrix of the star sensor body coordinate system relative to the inertial navigation system coordinate system is an attitude matrix represented by a three-dimensional installation error angle, i.e., a system state variable.

[0027] In step S1, the system state variables also include system noise; the system noise is the product of the system noise allocation matrix and the system noise vector; the measurement information of the system at a certain moment also includes measurement noise; the system noise and the measurement noise are both zero-mean Gaussian white noise.

[0028] In step S2, the specific steps are: S21: obtaining a one-step prediction of the state in the iterative extended Kalman filter according to the system state variables; S22: Substitute the one-step state prediction in the iterative extended Kalman filter into the nonlinear vector function and derive the measurement Jacobian matrix.

[0029] In step S3, the specific steps are: S31: Substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter, and perform pre-filtering according to the ordinary extended Kalman filter method; S32: Perform iterative filtering to complete the calibration of the installation error of the inertial astronomical integrated navigation system.

[0030] In this embodiment, the installation error between the inertial navigation and the star sensor affects the accuracy of the star sensor attitude measurement. The state space model and measurement model of the iterative extended Kalman filter are constructed through the star vector in the star sensor body coordinate system, the ephemeris star vector in the inertial coordinate system and the inertial information, and the measurement Jacobian matrix is ​​derived and calculated. The measurement Jacobian matrix is ​​substituted into the measurement model of the iterative extended Kalman filter to iteratively estimate the installation error of the inertial astronomical integrated navigation system, thereby realizing the function of calibrating the installation error of the inertial astronomical integrated navigation system.

[0031] Example 2 The steps of this embodiment are the same as those of Embodiment 1, except that each step is applied to a specific example. Specifically, the following steps are included: S1: Based on the star vector in the star sensor body coordinate system and the ephemeris star vector and inertial information measured by the star sensor, the state space model and measurement model of the iterative extended Kalman filter are constructed, specifically:

[0032] In the formula, For the system The state variable at the moment is selected as the three-dimensional installation error angle between the star sensor and the inertial navigation system, as follows:

[0033] in They are the pitch direction installation error angle, roll direction installation error angle, and heading direction installation error angle between the two. System The state of the moment.

[0034] is the system state transfer matrix. Since the inertial navigation system and the star sensor are installed in a fixed connection in the inertial astronomical integrated navigation system, the installation error angle is a constant, that is, is the third-order identity matrix.

[0035] Assign the matrix to the system noise, is the system noise vector.

[0036] for The measurement information at the moment is expressed as follows:

[0037] in is the star sensor body coordinate system, The system is an inertial coordinate system. is the inertial navigation system coordinate system, is the Earth coordinate system, The system is the navigation coordinate system (select the geographic coordinate system), It is the star vector in the star sensor body coordinate system measured by the star sensor. That is the ephemeris star vector in the corresponding inertial system, is the attitude matrix obtained by pure inertial navigation, is the matrix of the earth coordinate system expressed by the local longitude and latitude relative to the geographic coordinate system, is the matrix of the inertial system expressed in Greenwich sidereal time relative to the Earth's system.

[0038] For measurement information About system state variables The nonlinear vector function is expressed as follows:

[0039] in is the attitude matrix of the star sensor body coordinate system relative to the inertial navigation system coordinate system, which is composed of the three-dimensional installation error angle The posture matrix represented by is as follows:

[0040] is the measurement noise vector, the system noise and the measurement noise are both zero-mean Gaussian white noise, that is:

[0041] in is the system noise variance matrix, To measure the noise variance matrix, it is generally required is positive and is non-negative definite.

[0042] S2: Based on the state space model and measurement model of iterative extended Kalman filtering, the measurement Jacobian matrix is ​​derived and calculated. for:

[0043] in For the one-step prediction of the state in the iterative extended Kalman filter, it is expressed as follows:

[0044] is a nonlinear vector function The dimension in which it is located.

[0045] S3: Substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter to iteratively estimate the installation error of the inertial astronomical integrated navigation system.

[0046] The filtering equation is as follows: First, pre-filtering is performed according to the ordinary extended Kalman filter method, as follows:

[0047] in .

[0048] Then iterative filtering is performed as follows:

[0049] in .

[0050] This completes the calibration of the installation error of the inertial astronomical integrated navigation system.

[0051] This embodiment eliminates the influence of the installation error between the inertial navigation system and the star sensor on the star sensor attitude measurement, is suitable for the application occasions of combined navigation, and has good adaptability and high precision.

[0052] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0053] Example 3 This embodiment is used to implement the principle of the above method embodiment to construct a satellite inertial installation error calibration system based on iterative filtering, including a data acquisition submodule, a model construction submodule, a matrix calculation submodule and an error estimation submodule; The data acquisition submodule is used to obtain the star vector in the star sensor body coordinate system through the star sensor, obtain the corresponding ephemeris star vector in the inertial coordinate system, and obtain inertial information through the inertial navigation system; The model building submodule is used to build the state space model and measurement model of iterative extended Kalman filter according to the star vector in the star sensor body coordinate system, the ephemeris star vector in the inertial coordinate system and the inertial information; The matrix calculation submodule is used to derive and calculate the measurement Jacobian matrix based on the state space model and measurement model of iterative extended Kalman filtering; The error estimation submodule is used to substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter to iteratively estimate the installation error of the inertial astronomical integrated navigation system.

[0054] Each sub-module is mainly used to implement each step of the method embodiment, which will not be described in detail here.

[0055] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0056] This embodiment also includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a method for calibrating a satellite inertial installation error based on iterative filtering.

[0057] This embodiment also provides a computer-readable storage medium on which executable instructions are stored. When the instructions are executed by a processor, the processor implements a satellite inertial installation error calibration method based on iterative filtering.

[0058] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0059] Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0060] The present application is described with reference to the flowchart of the method and computer program product according to Embodiment 1 of the present application. It should be understood that each process in the flowchart or block diagram, and the combination of processes in the flowchart can be implemented by computer program instructions.

[0061] These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A star inertial installation error calibration system based on iterative filtering and a function specified in one or more processes.

[0062] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A function specified in a process or multiple processes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 The invention provides steps of a method for calibrating a satellite inertial installation error based on iterative filtering specified in a process or multiple processes.

[0064] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. A method for calibrating satellite inertial installation error based on iterative filtering, characterized in that: The following steps are involved: S0: Obtain the star vector in the star sensor body coordinate system through the star sensor, obtain the corresponding ephemeris star vector in the inertial coordinate system, and obtain inertial information through the inertial navigation system; S1: Construct the state space model and measurement model of iterative extended Kalman filter based on the star vector in the star sensor body coordinate system, the ephemeris star vector in the inertial coordinate system and the inertial information; S2: Based on the state space model and measurement model of iterative extended Kalman filter, the measurement Jacobian matrix is ​​derived and calculated; S3: Substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter to iteratively estimate the installation error of the inertial astronomical integrated navigation system.

2. The method for calibrating satellite inertial installation error based on iterative filtering according to claim 1, characterized in that: In the step S1, the specific steps are: S11: Select the three-dimensional installation error angle between the star sensor and the inertial navigation system as the system state variable of the input model; the system state variable at the current moment includes the product of the system state variable at the previous moment and the system state transfer matrix; the system state transfer matrix is ​​a third-order unit matrix; S12: The measurement information output by the model at a certain moment is a nonlinear vector function of the system state variables, which is specifically expressed as the difference between the star vector in the star sensor body coordinate system and the star vector in the inertial navigation system coordinate system.

3. The method for calibrating satellite inertial installation error based on iterative filtering according to claim 2 is characterized in that: In the step S11, the three-dimensional installation error angle includes a pitch direction installation error angle, a roll direction installation error angle and a heading direction installation error angle.

4. The method for calibrating satellite inertial installation error based on iterative filtering according to claim 2, characterized in that: In step S12, the star vector in the inertial navigation system coordinate system is the product of the attitude matrix obtained by pure inertial navigation, the matrix of the earth coordinate system represented by the local longitude and latitude relative to the geographic coordinate system, the matrix of the inertial system represented by Greenwich sidereal time relative to the earth system, and the ephemeris star vector in the inertial coordinate system corresponding to the star sensor body coordinate system.

5. The method for calibrating satellite inertial installation error based on iterative filtering according to claim 4, characterized in that: In the step S12, the product is expressed as the product of the attitude matrix of the star sensor body coordinate system relative to the inertial navigation system coordinate system and the star vector in the star sensor body coordinate system; the attitude matrix of the star sensor body coordinate system relative to the inertial navigation system coordinate system is an attitude matrix represented by a three-dimensional installation error angle, i.e., a system state variable.

6. The method for calibrating satellite inertial installation error based on iterative filtering according to claim 2, characterized in that: In the step S1, The system state variables also include system noise; the system noise is the product of the system noise distribution matrix and the system noise vector; The measurement information of the system at a certain moment also includes measurement noise; Both the system noise and the measurement noise are zero-mean Gaussian white noise.

7. The method for calibrating satellite inertial installation error based on iterative filtering according to claim 1, characterized in that: In the step S2, the specific steps are: S21: obtaining a one-step prediction of the state in the iterative extended Kalman filter according to the system state variables; S22: Substitute the one-step state prediction in the iterative extended Kalman filter into the nonlinear vector function and derive the measurement Jacobian matrix.

8. The method for calibrating satellite inertial installation error based on iterative filtering according to claim 1, characterized in that: In the step S3, the specific steps are: S31: Substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter, and perform pre-filtering according to the ordinary extended Kalman filter method; S32: Perform iterative filtering to complete the calibration of the installation error of the inertial astronomical integrated navigation system.

9. A satellite inertial installation error calibration system based on iterative filtering, characterized in that: The data acquisition submodule is used to obtain the star vector in the star sensor body coordinate system through the star sensor, obtain the corresponding ephemeris star vector in the inertial coordinate system, and obtain inertial information through the inertial navigation system; The model building submodule is used to build the state space model and measurement model of iterative extended Kalman filter according to the star vector in the star sensor body coordinate system, the ephemeris star vector in the inertial coordinate system and the inertial information; The matrix calculation submodule is used to derive and calculate the measurement Jacobian matrix based on the state space model and measurement model of iterative extended Kalman filtering; The error estimation submodule is used to substitute the measurement Jacobian matrix into the measurement model of the iterative extended Kalman filter to iteratively estimate the installation error of the inertial astronomical integrated navigation system.

10. A computer memory, characterized in that: A computer program executable by a computer processor is stored therein, and the computer program executes a satellite inertial installation error calibration method based on iterative filtering as described in any one of claims 1 to 8.

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