Random target tracking estimation method for low-orbit star network

Abstract

The invention relates to a low-orbit star network-oriented random target tracking estimation method, which comprises the following steps of: establishing a dynamic filter AUKF of multiple targets in a geocentric inertial coordinate system by taking position, speed and thrust acceleration vectors of the targets j (j = 1, 2,..., Nt) in the geocentric inertial coordinate system as state vectors; establishing a maneuvering target space-based observation model based on the measurement equation of the target under the geocentric inertial coordinate system; based on the calculation result of the maneuvering target space-based observation model at the current moment, constructing a maneuvering starting and ending detector, when it is judged that the target starts maneuvering, using a maneuvering dynamics filter AUKF to track and estimate the position, speed and maneuvering acceleration information of the target, and when it is judged that the maneuvering of the target is ended, using the maneuvering dynamics filter AUKF to track and estimate the maneuvering speed of the target. And a standard Kalman filter UKF is switched to carry out state updating on the position and the speed of the target. According to the method, the relay observation and whole-course tracking performance estimation problems of the low-orbit star network on the uncertain maneuvering target are solved, and the method can be used for verification and evaluation of a large-scale star network system detection simulation test.

Description

Technical Field

[0001] The present invention belongs to the technical field of detection and tracking, and relates to a random target tracking and estimation method for a low-orbit satellite network. Background Art

[0002] The significant advantage of using low-orbit satellite network to track space maneuvering targets is relay observation and full-process tracking. However, due to the constant changes in the observation geometric relationship between the target and the satellite, the low-orbit satellite network faces two basic scientific problems in maintaining continuous tracking capabilities: one is the problem of autonomous scheduling of the satellite network, and the other is how to fuse the original detection data of different satellites in the satellite network and obtain high-precision flight trajectory information of the target through filtering estimation.

[0003] In view of the uncertainty of information such as target maneuvering time and maneuvering thrust, if the traditional multi-satellite centralized collaborative filter is used, when the multi-satellite observation mode changes dynamically, there will be problems such as inability to autonomously adjust the structure, difficulty in ensuring filtering stability, and divergence of target trajectory filtering estimates, making it difficult to correctly evaluate the effectiveness of target penetration. Summary of the invention

[0004] The technical problem solved by the present invention is: aiming at the relay observation and full-process tracking performance prediction problem of uncertain maneuvering targets by low-orbit star network, a random target tracking prediction method for low-orbit star network is proposed, which can be used in the verification and evaluation of large-scale star network system detection simulation experiments.

[0005] The technical solution of the present invention is: a method for tracking and estimating random targets in a low-orbit satellite network, comprising the following steps: By target j , j =1,2,…, N t The position, velocity and thrust acceleration vector in the geocentric inertial coordinate system are taken as state vectors, and a dynamic filter AUKF of multiple targets in the geocentric inertial coordinate system is established to filter and estimate the position, velocity and maneuvering acceleration of the target during maneuvering. N t for t Total number of targets at a time; Based on the measurement equation of the target in the geocentric inertial coordinate system, a space-based observation model for maneuvering targets is established; According to the influence of the measurement residual on the sensitivity of maneuvering, based on the calculation results of the maneuvering target space-based observation model at the current moment, a maneuvering start and end detector is constructed. When the output result of the maneuvering start and end detector is greater than a first preset threshold, it is considered that the target starts to maneuver, and the dynamic filter AUKF is used to track and estimate the position, velocity and maneuvering acceleration information of the target. When the output result of the maneuvering start and end detector is less than a second preset threshold, it is considered that the target maneuver is terminated, and the standard Kalman filter UKF is switched to update the position and velocity of the target.

[0006] Furthermore, the dynamic filter AUKF of multiple targets in the geocentric inertial coordinate system is established as follows: Take the target j The state vector of ,in , , The target j The position, velocity and thrust acceleration vector in the geocentric inertial coordinate system, the established maneuvering target dynamics filter AUKF is: (1) in, is the gravitational constant, represents the dynamics function, is the current radius of the Earth, Represents the system noise of the target.

[0007] Furthermore, the space-based observation model of mobile targets is established as follows: The low-orbit satellite network used for space-based observations adopts Walker- Constellation Program, Walker- The constellation is described by three parameters Ns / P / N, where Ns is the total number of satellites in the entire constellation, P represents the number of orbital planes of the constellation, and N represents the number of satellites in the orbital plane. It represents the orbital inclination. The value together with the Ns, P, and N parameters determine the entire constellation; each orbit has the same orbital inclination, and the nodes are evenly distributed at equal intervals; For the target j , if the following conditions are met, it can be detected by the satellite i Detected: (2) in, i Indicates the number of the satellite in the constellation. j Indicates the target number, Indicates satellite i Half field of view, Indicates satellitei and goals j According to the cosine theorem, the angle between them is converted into: (3) (4) in, is the satellite to target position vector, is the position vector from the satellite to the origin of the geocentric inertial coordinate system, is the vector from the origin of the geocentric inertial coordinate system to the satellite position, is the vector from the origin of the geocentric inertial coordinate system to the target position, Represents the position vector of the satellite in the geocentric inertial coordinate system; If the satellite and the target satisfy equations (3) to (4), it means that the target is within the detection range of the satellite. For a satellite carrying an optical sensor, the azimuth and pitch angle of the target in the satellite-fixed coordinate system can be obtained. According to the state vectors of the target and the satellite in the geocentric inertial coordinate system, the measurement equation of the target in the geocentric inertial coordinate system is obtained, that is, the space-based observation model of the maneuvering target is as follows: (5) in, is the measurement value obtained by the satellite detecting the target in the geocentric inertial coordinate system, is the measurement function, is the variance matrix of the measurement noise, is the satellite status value at time t, is the target state value at time t.

[0008] Furthermore, the maneuver start and end detector is as follows: (6) in, is a scalar, is the measurement value obtained by the satellite detecting the target at time t , is the measurement value obtained by the satellite detecting the target at the previous moment (t-1) , for The state dimension of is the variance weight of the sampling point, For satellite i Measurement sampling points, is the variance matrix of satellite measurement noise; The measurement residual is defined as , definition , Then equation (6) is converted to: (7) set up is the first preset threshold, and if , then the maneuver is considered to have started, and the maneuver target dynamics filter AUKF is used to track and estimate the target's position, velocity, and maneuver acceleration information. The state vector is taken as ; set up is the second preset threshold, and if , the maneuver is considered to be terminated, and the filter is switched to the standard Kalman filter UKF accordingly, and the state vector is taken as .

[0009] Furthermore, for constant-value maneuvering targets, the standard Kalman filter UKF is always used to update the state of the target position and velocity to ensure tracking accuracy.

[0010] A random target tracking and estimation system for low-orbit satellite network, including a dynamic filter AUKF module, a maneuvering target space-based observation model module, and a maneuvering start and end detector module; Dynamic filter AUKF module, used to target j , j =1,2,…, N t The position, velocity and thrust acceleration vector in the geocentric inertial coordinate system are taken as state vectors, and a dynamic filter AUKF of multiple targets in the geocentric inertial coordinate system is established to filter and estimate the position, velocity and maneuvering acceleration of the target during maneuvering. N t for t Total number of targets at a time; The maneuvering target space-based observation model module is used to establish a maneuvering target space-based observation model based on the measurement equation of the target in the geocentric inertial coordinate system; The maneuver start and end detector module is used to construct a maneuver start and end detector according to the sensitivity of the measurement residual to the maneuver and the calculation result of the maneuvering target space-based observation model module at the current moment. When the output result of the maneuver start and end detector is greater than the first preset threshold, it is considered that the target starts to maneuver, and the dynamic filter AUKF module is used to track and estimate the position, velocity and maneuver acceleration information of the target. When the output result of the maneuver start and end detector is less than the second preset threshold, it is considered that the target maneuver is terminated, and the standard Kalman filter UKF is switched to update the state of the target position and velocity.

[0011] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a method for tracking and estimating random targets in a low-orbit satellite network are implemented.

[0012] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for tracking and estimating random targets in a low-orbit satellite network are implemented.

[0013] A computer program product includes a computer program, which, when executed by a processor, implements the steps of a method for tracking and estimating random targets in a low-orbit satellite network.

[0014] The beneficial effects of the present invention compared with the prior art are: (1) The significant advantages of using a low-orbit satellite network to track space maneuvering targets are relay observation and full-process tracking. In view of the uncertainty of information such as target maneuvering time and maneuvering thrust, if a traditional multi-satellite centralized collaborative filter is used, when the multi-satellite observation mode changes dynamically, there will be problems such as inability to adjust the structure autonomously, difficulty in ensuring filtering stability, and divergence of target trajectory filter estimates, which makes it difficult to make a correct assessment of the effectiveness of target avoidance. The maneuvering target tracking estimation method for the low-orbit satellite network proposed in the present invention solves the problem of estimating the maneuverability performance of the two types of targets under the above-mentioned large-scale low-orbit satellite network detection conditions, and supports the evaluation of target avoidance effectiveness in large-scale satellite network system detection simulation tests. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for tracking and estimating random targets in a low-orbit satellite network according to the present invention; Figure 2 Schematic diagram of the satellite detection target visibility principle. DETAILED DESCRIPTION

[0016] The present invention proposes a method for tracking and estimating random targets in a low-orbit satellite network. Figure 1 As shown, the following steps are included: Step 1: Target j , j =1,2,…, N t The position, velocity and thrust acceleration vector in the geocentric inertial coordinate system are taken as state vectors, and a dynamic filter AUKF of multiple targets in the geocentric inertial coordinate system is established to filter and estimate the position, velocity and maneuvering acceleration of the target during maneuvering. N t for t Total number of targets at a time; Step 2: Based on the measurement equation of the target in the geocentric inertial coordinate system, a space-based observation model of the maneuvering target is established; Step three, according to the influence of the measurement residual on the sensitivity of maneuvering, based on the calculation results of the maneuvering target space-based observation model at the current moment, construct a maneuvering start and end detector, when the output result of the maneuvering start and end detector is greater than the first preset threshold, it is considered that the target starts to maneuver, and the dynamic filter AUKF is used to track and estimate the position, velocity and maneuvering acceleration information of the target, when the output result of the maneuvering start and end detector is less than the second preset threshold, it is considered that the target maneuver is terminated, and switch to using the standard Kalman filter UKF to update the state of the target position and velocity.

[0017] The method of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0018] Example 1 Step 1: Target j , j =1,2,…, N t The position, velocity and thrust acceleration vector in the geocentric inertial coordinate system are taken as state vectors, and a dynamic filter AUKF of multiple targets in the geocentric inertial coordinate system is established for filtering estimation of the position, velocity and maneuvering acceleration of the target during maneuvering.

[0019] After the maneuvering target leaves the atmosphere, it enters the free flight phase. Without considering aerodynamics and the rotation of the earth, the target's motion is only affected by gravity and engine thrust. Consider multiple targets with thrusters. j , j =1,2,…, N t The position and velocity components in the geocentric inertial coordinate system are the state vectors, that is, ,in N t for t The total number of targets at the moment.

[0020] Since the LEO satellite network system cannot know the maneuver sequence of the target in advance, for non-cooperative targets, the target is usually j The state vector of ,in , , The target j The position, velocity and thrust acceleration vector in the geocentric inertial coordinate system, the established maneuvering target dynamics filter AUKF is: (1) in, is the gravitational constant, represents the dynamics function, is the current radius of the Earth, Represents the system noise of the target.

[0021] Step 2: Based on the measurement equation of the target in the geocentric inertial coordinate system, a space-based observation model of the maneuvering target is established.

[0022] The low-orbit satellite network used for space-based observations adopts Walker- Constellation scheme. Walker- A constellation can be described by three parameters Ns / P / N, where Ns is the total number of satellites in the entire constellation, P represents the number of orbital planes of the constellation, and N represents the number of satellites in the orbital plane. It represents the orbital inclination. The value together with the Ns, P, and N parameters determine the entire constellation; each orbit has the same orbital inclination, and the nodes are evenly distributed at equal intervals.

[0023] For satellites equipped with optical sensors, when a target enters the satellite's detection range, such as Figure 2 As shown, the azimuth information of the target can be obtained.

[0024] For the target j , if the following conditions are met, it can be detected by the satellite i Detected: (2) in, i Indicates the number of the satellite in the constellation. j Indicates the target number, Indicates satellite i Half field of view, Indicates satellite i and goals j According to the cosine theorem, the angle between them can be written as: (3) (4) in, is the satellite to target position vector, is the position vector from the satellite to the origin of the geocentric inertial coordinate system, is the vector from the origin of the geocentric inertial coordinate system to the satellite position, is the vector from the origin of the geocentric inertial coordinate system to the target position, The position vector of the satellite in the Earth-centered inertial coordinate system.

[0025] If the satellite and the target satisfy equations (3) to (4), it means that the target is within the detection range of the satellite. For satellites carrying optical sensors, the azimuth and pitch angles of the target in the satellite-fixed coordinate system can be obtained. Based on the state vectors of the target and the satellite in the geocentric inertial coordinate system, the measurement equation of the target in the geocentric inertial coordinate system can be obtained: (5) in, is the measurement value obtained by the satellite detecting the target in the geocentric inertial coordinate system, is the measurement function, is the variance matrix of the measurement noise, is the satellite status value at time t, is the target state value at time t. Formula (5) is the established space-based observation model for maneuvering targets.

[0026] Step 3: Establish a variable structure filtering framework: construct a maneuver start and end detector. When it is determined that the target starts to maneuver, use the maneuvering target dynamics filter AUKF to track and estimate the target's position, velocity and maneuvering acceleration information. When it is determined that the target maneuver ends, switch to the standard Kalman filter UKF to update the target's position and velocity status.

[0027] Since the low-orbit satellite network cannot accurately know the target's maneuver sequence in advance, it is necessary to set up maneuver detectors to accurately detect the start and end of the maneuver.

[0028] The variable structure filtering framework does not need to run multiple filters at the same time, but uses a maneuver detector to determine the start and end of the maneuver, thereby realizing switching between different filters. The amount of calculation is small and it is suitable for the force scenario of the target in the present invention.

[0029] 301. Construction of maneuver start and end detector: In the initial stage of the target maneuver, the estimated covariance of the filter remains almost unchanged, but its measurement residual will increase. Therefore, according to the sensitivity of the measurement residual to the maneuver, the following maneuver start and end detector is constructed: (6) in, is a scalar, is the measurement value obtained by the satellite detecting the target at time t , is the measurement value obtained by the satellite detecting the target at the previous moment (t-1) , for The state dimension of is the variance weight of the sampling point, For satellite i Measurement sampling points, is the variance matrix of satellite measurement noise; The measurement residual is defined as , definition , Then formula (6) can be rewritten as: (7) Step 1: Setup is the first preset threshold, and if , then the maneuver is considered to have started, and the maneuver target dynamics filter AUKF is used to track and estimate the target's position, velocity, and maneuver acceleration information. The state vector is taken as .

[0030] Step 2: Settings is the second preset threshold, and if , the maneuver is considered to be terminated, and the filter is switched to the standard Kalman filter UKF accordingly, and the state vector is taken as .

[0031] 302. For an extraordinary maneuvering target, filter switching is performed according to the method of step 301 to track and estimate the position, velocity and maneuvering acceleration information of the target; For constant value maneuvering targets, the standard Kalman filter UKF is always used to update the state of the target position and velocity to ensure tracking accuracy.

[0032] The present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes Figure 1 The method described.

[0033] 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. 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 and optical storage, etc.) containing computer-usable program codes.

[0034] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. 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 generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0035] 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 including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0036] 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 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0037] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0038] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A method for tracking and estimating random targets in a low-orbit satellite network, characterized in that: The following steps are involved: By target j , j =1,2,…, N t The position, velocity and thrust acceleration vector in the geocentric inertial coordinate system are taken as state vectors, and a dynamic filter AUKF of multiple targets in the geocentric inertial coordinate system is established to filter and estimate the position, velocity and maneuvering acceleration of the target during maneuvering. N t for t Total number of targets at a time; Based on the measurement equation of the target in the geocentric inertial coordinate system, a space-based observation model for maneuvering targets is established; According to the influence of the measurement residual on the sensitivity of maneuvering, based on the calculation results of the maneuvering target space-based observation model at the current moment, a maneuvering start and end detector is constructed. When the output result of the maneuvering start and end detector is greater than a first preset threshold, it is considered that the target starts to maneuver, and the dynamic filter AUKF is used to track and estimate the position, velocity and maneuvering acceleration information of the target. When the output result of the maneuvering start and end detector is less than a second preset threshold, it is considered that the target maneuver is terminated, and the standard Kalman filter UKF is switched to update the position and velocity of the target.

2. According to claim 1, a method for tracking and estimating random targets in a low-orbit satellite network is characterized in that: The dynamic filter AUKF of multiple targets in the geocentric inertial coordinate system is established as follows: Take the target j The state vector of ,in , , The target j The position, velocity and thrust acceleration vector in the geocentric inertial coordinate system, the established maneuvering target dynamics filter AUKF is: （1） in, is the gravitational constant, represents the dynamics function, is the current radius of the Earth, Represents the system noise of the target.

3. The method for tracking and estimating random targets in a low-orbit satellite network according to claim 1, characterized in that: The space-based observation model of mobile targets is established as follows: The low-orbit satellite network used for space-based observations adopts Walker- Constellation Program, Walker- The constellation is described by three parameters Ns / P / N, where Ns is the total number of satellites in the entire constellation, P represents the number of orbital planes of the constellation, and N represents the number of satellites in the orbital plane. It represents the orbital inclination. The value together with the Ns, P, and N parameters determine the entire constellation; each orbit has the same orbital inclination, and the nodes are evenly distributed at equal intervals; For the target j , if the following conditions are met, it can be detected by the satellite i Detected: （2） in, i Indicates the number of the satellite in the constellation. j Indicates the target number, Indicates satellite i Half field of view, Indicates satellite i and goals j According to the cosine theorem, the angle between them is converted into: （3） （4） in, is the satellite to target position vector, is the position vector from the satellite to the origin of the geocentric inertial coordinate system, is the vector from the origin of the geocentric inertial coordinate system to the satellite position, is the vector from the origin of the geocentric inertial coordinate system to the target position, Represents the position vector of the satellite in the geocentric inertial coordinate system; If the satellite and the target satisfy equations (3) to (4), it means that the target is within the detection range of the satellite. For a satellite carrying an optical sensor, the azimuth and pitch angle of the target in the satellite-fixed coordinate system can be obtained. According to the state vectors of the target and the satellite in the geocentric inertial coordinate system, the measurement equation of the target in the geocentric inertial coordinate system is obtained, that is, the space-based observation model of the maneuvering target is as follows: （5） in, is the measurement value obtained by the satellite detecting the target in the geocentric inertial coordinate system, is the measurement function, is the variance matrix of the measurement noise, is the satellite status value at time t, is the target state value at time t.

4. The method for tracking and estimating random targets in a low-orbit satellite network according to claim 3, characterized in that: The maneuver start and end detectors are as follows: （6） in, is a scalar, is the measurement value obtained by the satellite detecting the target at time t , is the measurement value obtained by the satellite detecting the target at the previous moment (t-1) , for The state dimension of is the variance weight of the sampling point, For satellite i Measurement sampling points, is the variance matrix of satellite measurement noise; The measurement residual is defined as , definition , Then equation (6) is converted to: （7） set up is the first preset threshold, and if , then the maneuver is considered to have started, and the maneuver target dynamics filter AUKF is used to track and estimate the target's position, velocity, and maneuver acceleration information. The state vector is taken as ; set up is the second preset threshold, and if , the maneuver is considered to be terminated, and the filter is switched to the standard Kalman filter UKF accordingly, and the state vector is taken as .

5. The method for tracking and estimating random targets in a low-orbit satellite network according to claim 1, characterized in that: For constant value maneuvering targets, the standard Kalman filter UKF is always used to update the state of the target position and velocity to ensure tracking accuracy.

6. A random target tracking and estimation system for low-orbit satellite network based on the method according to any one of claims 1 to 5, characterized in that: It includes the AUKF module, the maneuvering target space-based observation model module, and the maneuvering start and end detector module; Dynamic filter AUKF module, used to target j , j =1,2,…, N t The position, velocity and thrust acceleration vector in the geocentric inertial coordinate system are taken as state vectors, and a dynamic filter AUKF of multiple targets in the geocentric inertial coordinate system is established to filter and estimate the position, velocity and maneuvering acceleration of the target during maneuvering. N t for t Total number of targets at a time; The maneuvering target space-based observation model module is used to establish a maneuvering target space-based observation model based on the measurement equation of the target in the geocentric inertial coordinate system; The maneuver start and end detector module is used to construct a maneuver start and end detector according to the sensitivity of the measurement residual to the maneuver and the calculation result of the maneuvering target space-based observation model module at the current moment. When the output result of the maneuver start and end detector is greater than the first preset threshold, it is considered that the target starts to maneuver, and the dynamic filter AUKF module is used to track and estimate the position, velocity and maneuver acceleration information of the target. When the output result of the maneuver start and end detector is less than the second preset threshold, it is considered that the target maneuver is terminated, and the standard Kalman filter UKF is switched to update the state of the target position and velocity.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 5 are implemented.

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