A Target Tracking Method and System with Multi-Coverage Fusion for Space-Based Networked Radar
By deploying multiple radar nodes in low Earth orbit and performing data preprocessing and covariance cross-fusion, the problem of insufficient coverage by a single space-based radar was solved, enabling efficient tracking and surveillance of low Earth orbit.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-17
- Publication Date
- 2026-04-03
AI Technical Summary
A single space-based radar cannot achieve full coverage of the low Earth orbit region and requires high-power aperture product to achieve large-area search. The key is how to effectively utilize multiple space-based radars for coordinated detection.
By deploying multiple radar nodes on each orbital plane in low Earth orbit, target echo data is acquired, preprocessed, and then subjected to unscented Kalman filtering. Finally, a covariance cross-fusion algorithm is used for sequential fusion to achieve target tracking.
It improves the tracking accuracy of space targets and the situational awareness capability of space surveillance systems, reduces the demand for power aperture product, and achieves comprehensive coverage of low Earth orbit space.
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Figure CN119511223B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of space situational awareness technology, and in particular to a target tracking method and system based on multi-coverage fusion of space-based networked radar. Background Technology
[0002] The number of space targets has exploded with the increase in human space activities, and tracking and monitoring technologies for various spacecraft have become an important technical support means for space situational awareness missions. Enhancing space situational awareness capabilities is key to space resource development, space security, and space control.
[0003] The primary task of a space target surveillance system is to accurately track and catalog important space targets. Space-based radar, as a crucial component of this system, can work in conjunction with ground-based radar and space-based optical sensors to further enhance the situational awareness capabilities of the space surveillance system. After years of development, various countries have made further breakthroughs in deployable antenna technology and T / R module technology for space-based radar. This effectively solves the problem of power transmission for space-based radar, making the manufacturing, equipment, and operation of space-based surveillance network radars possible.
[0004] Unlike space-based optical sensors and ground-based radar, space-based networked radar possesses unique detection advantages. Space-based radar is not subject to geographical limitations imposed by different countries, enabling its deployment across the entire sky. Furthermore, unlike space-based optical sensors, its detection capabilities are unaffected by sunlight, allowing for effective detection of space targets even in areas under Earth's shadow. These characteristics ensure a higher observation frequency for space targets, significantly enhancing the surveillance system's ability to update its cataloging information on these targets.
[0005] However, space-based radar also faces challenges such as: the large field of view required for space target surveillance, necessitating extensive searches, places high demands on the radar's power-aperture product; and the insufficient capacity of a single space-based radar to achieve large-scale low-Earth orbit (LEO) space target detection, necessitating the construction of a Walker constellation for comprehensive LEO coverage. In this scenario, space targets may appear in the surveillance airspace of multiple space-based radars, making the effective utilization of sensor resources for coordinated detection a key research area. Summary of the Invention
[0006] Therefore, it is necessary to provide a target tracking method and system that integrates multiple coverage of space-based networked radar, capable of conducting large-scale searches of space targets and providing comprehensive coverage of low-Earth orbit space, to address the aforementioned technical problems.
[0007] A target tracking method based on multi-coverage fusion of space-based networked radar, the method comprising:
[0008] Acquire target echo data from multiple radar nodes deployed on various orbital planes of low Earth orbit, which observe the same target.
[0009] Radar measurement data is obtained by preprocessing the echo data of each target, wherein the measurement data of each target is three-dimensional measurement data in a Cartesian coordinate system with the target as the origin;
[0010] Each of the three-dimensional measurement data is subjected to unscented Kalman filtering to obtain multiple corresponding filtered data.
[0011] The covariance cross-fusion algorithm is used to sequentially fuse all the filtered data to obtain the target's fusion state estimate, thereby enabling target tracking.
[0012] In one embodiment, the preprocessing of the target echo data includes pulse compression and moving target detection.
[0013] In one embodiment, the three-dimensional measurement data is represented as:
[0014]
[0015]
[0016] Where θq,k(xk)=arctan2((yk-yq,k) / (xk-xq,k))
[0017]
[0018] In the above formula, R q,k ,θ q,k , Let x represent the target range, azimuth angle, and elevation angle information in the measurement matrix of the q-th radar node at time k, respectively. k This represents the motion state of the target at time k, (x q,k y q,k , z q,k (x) represents the position coordinates of the q-th radar node in the Cartesian coordinate system at time k. k ,y k ,z k ) represents the position coordinates of the target in the Cartesian coordinate system at time k.
[0019] In one embodiment, the motion state of the target is represented as:
[0020] x k =Fxk-1 +u k-1
[0021] In the above formula, x k The motion state vector of the target is represented in the following form: Among them, (x k ,y k ,z k ), Let u represent the position, velocity, and acceleration at time k in Cartesian coordinates, respectively, and let F represent the state transition matrix. k-1 This represents Gaussian white noise with zero mean.
[0022] In one embodiment, all the filtered data are sequentially fused according to a covariance cross-fusion algorithm, and the fused target state estimate and its corresponding covariance representation are:
[0023]
[0024] In the above formula, Let k represent the set of all tracking radar nodes of the target at time k. This represents the fusion estimation state of space targets. ω represents the fusion covariance of the tracking. q,k This represents the weight of different radar nodes during the fusion process.
[0025] In one embodiment, the weights of different radar nodes in the fusion process are obtained using the following formula:
[0026]
[0027] This application also provides a target tracking system with multi-coverage fusion of space-based networked radar, the system comprising: multiple space-based radars arranged on various orbital planes in low Earth orbit, and an information fusion unit;
[0028] Each of the aforementioned space-based radars is used to observe target echo data obtained from the same target. After preprocessing the target echo data, radar measurement data is obtained. The target measurement data is three-dimensional measurement data in a Cartesian coordinate system with the target as the origin. After performing unscented Kalman filtering on the three-dimensional measurement data, corresponding filtered data is obtained, and the filtered data is sent to the information fusion unit.
[0029] The information fusion unit is used to sequentially fuse all the filtered data according to the covariance cross-fusion algorithm to obtain the target fusion state estimate, so as to achieve target tracking.
[0030] In one embodiment, the space-based radars are networked in a Walker constellation geometry configuration.
[0031] In one embodiment, the radar power aperture product of each of the space-based radars is obtained based on the dual-track element (TEL) information.
[0032] The aforementioned target tracking method and system based on multi-coverage fusion of space-based networked radars preprocesses target echo data obtained from observations of the same target by multiple radar nodes deployed on various orbital planes in low Earth orbit. This preprocessing yields radar measurement data, where each target measurement data is a three-dimensional measurement in a Cartesian coordinate system with the target as the origin. Each three-dimensional measurement data is then subjected to unscented Kalman filtering to obtain multiple filtered data sets. These filtered data sets are then sequentially fused using a covariance cross-fusion algorithm to obtain a fused state estimate of the target, thus enabling target tracking. This method can achieve accurate tracking of space targets. Attached Figure Description
[0033] Figure 1 This is a flowchart illustrating a target tracking method using multi-coverage fusion of space-based networked radar in one embodiment.
[0034] Figure 2 This is a schematic diagram of the observation geometry of a space-based network radar in one embodiment;
[0035] Figure 3 This is a schematic diagram of space-based radar surveillance in one embodiment;
[0036] Figure 4 This is a schematic diagram of the power aperture product as a function of detection distance in a simulation experiment.
[0037] Figure 5 This is a schematic diagram of the power demand as a function of the effective aperture in a simulation experiment.
[0038] Figure 6 This is a schematic diagram of the Walker constellation, a space-based radar, in a simulation experiment.
[0039] Figure 7 This is a schematic diagram of target orbit coverage in a simulation experiment;
[0040] Figure 8 This is a schematic diagram of the coverage multiplicity quality parameter in a simulation experiment.
[0041] Figure 9 This is a schematic diagram comparing tracking position errors in a simulation experiment;
[0042] Figure 10 This is a schematic diagram comparing tracking speed errors in a simulation experiment;
[0043] Figure 11This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0045] To address the problem that a single space-based radar cannot achieve comprehensive coverage of the low Earth orbit region in existing technologies, this application provides a target tracking method that integrates multiple coverages of space-based networked radars, specifically including the following steps:
[0046] Step S100: Obtain target echo data from multiple radar nodes deployed on various orbital planes of the space-based network radar, which observe the same target.
[0047] Step S110: After preprocessing the echo data of each target, radar measurement data is obtained. The measurement data of each target is three-dimensional measurement data in a Cartesian coordinate system with the target as the origin.
[0048] Step S120: Perform unscented Kalman filtering on each of the three-dimensional measurement data to obtain multiple corresponding filtered data.
[0049] Step S130: Sequentially fuse all the filtered data according to the covariance cross-fusion algorithm to obtain the target fusion state estimate, so as to achieve target tracking.
[0050] In this embodiment, an algorithm is provided for target tracking in a space-based network radar by fusing observation data of multiple space-based radars in their respective fields of view for the same target.
[0051] In this embodiment, the observation geometry diagram of space-based network radar monitoring space targets is shown in Title 2. Space targets appear simultaneously in the observation airspace of multiple space-based radars, and the space-based radar system can improve the tracking accuracy of space targets through cooperative tracking.
[0052] like Figure 2 As shown, the space-based radar and the space target operate simultaneously in three-dimensional space. Through coordinate transformation, the problem of space-based radar tracking the space target can be transformed into a multi-sensor target tracking problem. Within a short time interval, the motion equation of the space target can be modeled as a CA model, and the target's motion state can be expressed as:
[0053] x k =Fx k-1 +u k-1 (1)
[0054] In formula (1), x k The motion state vector of the target at time k is represented by the following form: Among them, (x k ,y k ,z k ), Let u represent the position, velocity, and acceleration at time k in Cartesian coordinates, respectively, and let F represent the state transition matrix. k-1 This represents Gaussian white noise with zero mean.
[0055] In step S110, after each space-based radar acquires the target echo data, the data is sequentially processed including pulse compression and moving target detection to obtain radar measurement data. The target measurement data is three-dimensional measurement data in a Cartesian coordinate system with the target as the origin.
[0056] In this embodiment, after preprocessing the target echo data, a series of radar measurements can be generated. At time k, the Q-th space-based radar simultaneously tracks the target, and the relationship between the observation vector of the q-th radar and the target state vector is as follows:
[0057]
[0058] In formula (2), This indicates the measurement noise of each space-based radar system. Let be the measurement matrix of the q-th radar at time k, which includes target range, azimuth angle information and elevation angle information.
[0059] Furthermore, the three-dimensional measurement data is represented as follows:
[0060]
[0061] Where, θq,k(xk)=arctan2((yk-yq,k) / (xk-xq,k))(4)
[0062]
[0063] In formulas (3) and (4), R q,k ,θ q,k , Let x represent the target range, azimuth angle, and elevation angle information in the measurement matrix of the q-th radar node at time k, respectively. k This represents the motion state of the target at time k, (x q,k y q,k , z q,k (x) represents the position coordinates of the q-th radar node in the Cartesian coordinate system at time k. k,y k ,z k ) represents the position coordinates of the target in the Cartesian coordinate system at time k.
[0064] In step S130, all filtered data are sequentially fused according to the covariance cross-fusion algorithm. The fused target state estimate and its corresponding covariance representation are as follows:
[0065]
[0066] In formula (5), Let k represent the set of all tracking radar nodes of the target at time k. This represents the fusion estimation state of space targets. ω represents the fusion covariance of the tracking. q,k This represents the weight of different radar nodes during the fusion process.
[0067] Furthermore, in solving the weights ω of different radar nodes during the fusion process... q,k At this time, the sum of the weights of all radar nodes is 1, and the minimum fusion covariance index is satisfied, using the following formula:
[0068]
[0069] In this embodiment, when fusing all filtered data, sequential processing is performed according to the data at the corresponding time point.
[0070] The distributed fusion scheme proposed in this application has low computational complexity and high stability, which can improve the tracking accuracy of space-based networked radar to a certain extent, and at the same time expand the situational awareness capability of space surveillance systems for space targets.
[0071] In this embodiment, the target tracking method of multi-coverage fusion of space-based networked radar can also be implemented in ground-based networked radar. Multiple ground-based radars can use the above tracking method to track the same airborne target and achieve good tracking accuracy.
[0072] This application also provides a target tracking system with multi-coverage fusion of space-based networked radars, which includes: multiple space-based radars arranged on various orbital planes in low Earth orbit, and an information fusion unit.
[0073] Each space-based radar is used to observe the target echo data of the same target. After preprocessing the target echo data, radar measurement data is obtained. The target measurement data is three-dimensional measurement data in a Cartesian coordinate system with the target as the origin. After performing unscented Kalman filtering on the three-dimensional measurement data, the corresponding filtered data is obtained and sent to the information fusion unit.
[0074] The information fusion unit is used to sequentially fuse all filtered data according to the covariance cross-fusion algorithm to obtain the target's fusion state estimate, so as to achieve target tracking.
[0075] To meet the increasingly stringent needs for space target surveillance and to form a large-scale space-based radar observation system, it is necessary to deploy multiple space-based radar satellites in space orbit to form a networked collaborative observation capability, and to complete the timely surveillance of key space targets through a "network-to-network" approach.
[0076] In this embodiment, the space-based radars are networked using a Walker constellation geometric configuration.
[0077] The Walker constellation is a space-based radar network composed of multiple satellites, designed to achieve comprehensive monitoring and data acquisition of various regions in low Earth orbit. The Walker constellation configuration is commonly described as (N / P / F, h, i). N is the total number of satellites in the constellation, P is the number of orbital planes in the constellation, i is the orbital inclination of the satellites in the constellation, h is the orbital altitude of the satellites in the constellation, and F is the phase factor, an integer between 0 and (P:1), representing the phase relationship between corresponding satellites on two adjacent orbital planes in the constellation.
[0078] Specifically, the phase factor is used to determine the phase difference between corresponding satellites on two adjacent orbital planes, and the offset angle between the two satellites is:
[0079]
[0080] These satellites, distributed in different orbits, cover various regions of low Earth orbit, enabling real-time monitoring of targets in low Earth orbit. Through the Walker constellation, high-frequency data acquisition is achieved, providing crucial support and data for monitoring key space targets.
[0081] Considering that the main limiting factor for the performance of space-based radar systems is the power aperture product, analyzing the power aperture product of monostatic space-based radars is crucial for the construction and design of space-based radar systems.
[0082] In this embodiment, the radar power aperture product of each space-based radar is obtained based on the dual-track element (TEL) information. Guided by TEL information, the space-based radar system can significantly reduce the search range of space targets, thereby effectively reducing the power aperture product requirement for space target surveillance missions.
[0083] Specifically, the ground-based surveillance system possesses a relatively complete cataloging database of key space targets, namely, the TLE (Trajectory Elements) information. Space-based radar can utilize this TLE information to predict the position and operational status of space targets with relatively high accuracy. The space-based radar only needs to reacquire the target within the prediction error range to achieve effective tracking of the space target. Figure 3 This is a schematic diagram of space-based radar monitoring of space targets. Assume the TLE prediction error is ΔR. TLE Let R be the radar detection range. Then, the angles corresponding to the radar search area in the azimuth and elevation dimensions of a space-based radar can be expressed as Θ. A =Θ E =2ΔR TLE / R.
[0084] At this point, the search solid angle of the space-based surveillance radar, expressed in sphericities, is:
[0085]
[0086] The 3dB beamwidths of the space-based radar antenna in the azimuth and elevation dimensions are θ, respectively. a With θ e Therefore, the number of antenna beam positions required to cover the solid angle Ω is n. B for:
[0087]
[0088] Assuming the space-based radar illuminates N pulses at a single wavelength, considering τ = 1 / B and P... t =P av T PRI / τ, the search radar equations (based on a single pulse per beam per PRI) can be reduced to:
[0089]
[0090] In formula (10), Δsnr represents the signal-to-noise ratio corresponding to a single pulse, P av B represents the power of the space-based radar, σ represents the bandwidth, and T represents the target cross-section. PRI Let λ represent the pulse repetition time, G represent the antenna gain, λ represent the wavelength, τ represent the pulse width, k represent the Boltzmann constant, and T represent the pulse repetition time. e The value represents the radar noise temperature, F represents the noise figure, and L represents the system loss.
[0091] When the radar scans the solid angle Ω, the required scanning time is T. sc Furthermore, when a single wave position illuminates N pulses, the radiation time T of the space-based radar on the space target is... i for:
[0092]
[0093] Combining formula (10), we get:
[0094]
[0095] When N pulses of a single wavelength are accumulated, the signal-to-noise ratio of a single wavelength is:
[0096]
[0097] Wherein, SNR is the cumulative detectable signal-to-noise ratio.
[0098] Furthermore, considering the relation G = 4πA / λ 2 and G = 4π / θ a θ e Formula (13) can be simplified to:
[0099]
[0100] In formula (14), P av A represents the power aperture product. The power aperture product (PAP) of space-based radar guided by TLE information can be obtained as follows:
[0101]
[0102] As can be seen from formula (15), when the TLE prediction error and the target scattering cross section are fixed, the transmit average power aperture product of a monostatic space-based radar is proportional to the square of the detection range. When the TLE prediction error and the detection range are fixed, the transmit average power aperture product of a monostatic space-based radar is inversely proportional to the target scattering cross section.
[0103] Specific limitations regarding the target tracking system for multi-coverage fusion of space-based networked radar can be found in the above description of the target tracking method for multi-coverage fusion of space-based networked radar, and will not be repeated here. Each module in the aforementioned target tracking system for multi-coverage fusion of space-based networked radar can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.
[0104] This paper also demonstrates the effectiveness of TLE information-guided calculation of the power aperture product of space-based radar through simulation experiments. The experiment sets the TLE prediction error to ΔR. TLE =20km, RCS of space target set to 1m 2According to formula (10), the curves showing the change of power aperture product with detection distance under different detection distances are shown below. Figure 4 As shown, when the distance changes from 1000km to 2000km, the power aperture product requirement changes from 41.50dB to 53.54dB.
[0105] Furthermore, the curves showing the variation of space-based radar power requirements with effective aperture at a detection range of 2000 km are presented, such as... Figure 5 As shown. When the effective aperture area is 50m² 2 At that time, the power requirement of space-based radar was 4523W, which is achievable with the current level of radar devices.
[0106] Next, simulation experiments were conducted to verify the coverage performance of the Walker constellation for low-Earth orbit targets. The experiment set the detection range of the space-based radar to 2000km, and the radar network consisted of 48 satellites with the same orbital inclination, whose corresponding constellation code was (48 / 8 / 1, 6854km, 50°). Figure 6 This is a schematic diagram of the Walker constellation of space-based radar. The simulation period was set from 28 Apr 2024 00:00:00.000 to 29 Apr 2024 00:00:00.000. The TLE data for the seed satellites of the space-based radar network and low-Earth orbit space targets are shown in Table 1.
[0107] Table 1. Six orbital elements of the SBR seed and LEO satellite
[0108]
[0109] Figure 7 This is a coverage performance diagram for low Earth orbit (LEO) targets from the Walker constellation. Analysis of the diagram shows that the Walker space-based radar network, coded as 48 / 8 / 1, 6854 km, 50°, can cover most of the orbital arc of LEO targets. Aerospace software calculations show that the space-based radar network achieves 87.7% orbital coverage of the target, with a maximum revisit time of 757.64 s.
[0110] Figure 8This is a figure of merit showing the coverage multiples of space-based network radar for low-Earth orbit targets. The figure shows that between 28 Apr 2024 00:00:00.000 and 29 Apr 2024 00:00:00.000, the space-based network radar can achieve triple coverage of space targets in a few time intervals, with the probability of single-layer coverage being roughly the same as that of double-layer coverage. During the double-layer and triple-layer coverage processes, utilizing effective multi-sensor fusion tracking algorithms can further improve the cataloging accuracy of space targets.
[0111] In this paper, the target tracking method proposed in this paper is implemented based on the proposed space-based networked radar multi-coverage fusion target tracking system, and experimental simulations are conducted. The tracking time interval for space targets by the space-based networked radar is set to: 28 Apr 2024 00:11:00.000~28 Apr 2024 00:12:00.000. The performance of the algorithm is verified by comparing the position root mean square error (RMSE) and velocity RMSE corresponding to the entire tracking process. The target root mean square error is defined as follows:
[0112]
[0113] In the above formula, MC represents the number of Monte Carlo simulations. and These are the estimated target position and target velocity, respectively.
[0114] Combination Figure 8 It can be seen that within this time interval, the space-based network radars successively achieved single-layer and double-layer coverage of the space target. Specifically, from 28 Apr 2024 00:11:00.000 to 28 Apr 2024 00:11:26.000, space-based radar 1 achieved single-layer observation of the space target; from 28 Apr 2024 00:11:26.000 to 28 Apr 2024 00:12:00.000, space-based radar 1 and space-based radar 2 jointly achieved double-layer coverage of the space target. We verify the effectiveness of the proposed collaborative observation of space-based network radars in improving the tracking performance of space targets by comparing the single-layer coverage tracking results of space-based radar 1 alone with the double-layer coverage tracking results formed by space-based radar 1 and space-based radar 2. Figure 9 and Figure 10 These are the position RMSE and velocity RMSE corresponding to the entire tracking process, respectively.
[0115] It can be seen that the entire tracking process of the space-based network radar on space targets is convergent. By frame 26, the coverage of the space-based network radar on the space target changes from single-layer coverage to double-layer coverage. At this point, since both radars in the space-based network radar can form state estimates of the space target, the optimal fused estimate can be effectively formed through the covariance cross-fusion algorithm, thereby further reducing the tracking error of the space target. Figure 10 As can be seen, at frame 60, the RMSE of the target position estimation by Space Radar 1 is 205.956m, while the RMSE of the position estimation by the proposed multi-coverage tracking algorithm is 169.389m, representing an improvement in position estimation accuracy of approximately 17.56%. Figure 10 As can be seen, at frame 60, the RMSE of the velocity estimation of the target by Space Radar 1 is 38.314 m / s, while the RMSE of the velocity estimation by the algorithm proposed in this paper is 32.964 m / s, representing an improvement in velocity estimation accuracy of approximately 13.96%. The proposed algorithm can effectively fuse the target state estimation information from multiple sensors during the tracking process, thereby improving the tracking performance of space targets in the space-based networked radar system to a certain extent.
[0116] In the aforementioned target tracking system with multi-coverage fusion of space-based networked radars, the radar power-aperture product formula under TLE information guidance was analyzed in detail, taking into account the special characteristics of space target surveillance. Furthermore, the CI fusion criterion was utilized to achieve collaborative tracking of low-Earth orbit (LEO) space targets by the space-based networked radars. By deploying the LEO Walker constellation space-based radars, the orbital coverage of LEO space targets can be improved, and collaborative networked operation of space-based surveillance radars can be achieved, effectively enhancing the accuracy and efficiency of space target surveillance and cataloging.
[0117] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0118] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 11As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a target tracking method for multi-coverage fusion of space-based networked radar. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.
[0119] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0120] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0121] Acquire target echo data from multiple radar nodes deployed on various orbital planes of low Earth orbit, which observe the same target.
[0122] Radar measurement data is obtained by preprocessing the echo data of each target, wherein the measurement data of each target is three-dimensional measurement data in a Cartesian coordinate system with the target as the origin;
[0123] Each of the three-dimensional measurement data is subjected to unscented Kalman filtering to obtain multiple corresponding filtered data.
[0124] The covariance cross-fusion algorithm is used to sequentially fuse all the filtered data to obtain the target's fusion state estimate, thereby enabling target tracking.
[0125] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:
[0126] Acquire target echo data from multiple radar nodes deployed on various orbital planes of low Earth orbit, which observe the same target.
[0127] Radar measurement data is obtained by preprocessing the echo data of each target, wherein the measurement data of each target is three-dimensional measurement data in a Cartesian coordinate system with the target as the origin;
[0128] Each of the three-dimensional measurement data is subjected to unscented Kalman filtering to obtain multiple corresponding filtered data.
[0129] The covariance cross-fusion algorithm is used to sequentially fuse all the filtered data to obtain the target's fusion state estimate, thereby enabling target tracking.
[0130] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0131] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0132] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
Claims
1. A target tracking method using multi-coverage fusion in space-based networked radar, characterized in that, The method includes: Target echo data from multiple radar nodes deployed on various orbital planes in low Earth orbit are obtained by observing the same target. These radar nodes are networked using a Walker constellation geometry. The radar power-aperture product of each node is obtained based on the dual-row orbital element TEL information. The space-based radar power-aperture product guided by the dual-row orbital element TEL information is expressed as: ； In the above formula, Indicates the signal-to-noise ratio per wavelet. This represents Boltzmann's constant. Indicates radar noise temperature. Represents the noise figure. Indicates system loss. Indicates the target scattering cross section. R Indicates the radar detection range. This indicates the TLE prediction error. Indicates the radar scanning solid angle Required scan time; After preprocessing the echo data of each target, radar measurement data is obtained. The measurement data of each target is three-dimensional measurement data in a Cartesian coordinate system with the target as the origin. Each of the three-dimensional measurement data is subjected to unscented Kalman filtering to obtain multiple corresponding filtered data. The covariance cross-fusion algorithm is used to sequentially fuse all the filtered data to obtain the target's fusion state estimate, thereby enabling target tracking.
2. The target tracking method according to claim 1, characterized in that, The preprocessing of the target echo data includes pulse compression and moving target detection.
3. The target tracking method according to claim 1, characterized in that, The three-dimensional measurement data is represented as follows: ； in, ; In the above formula, They represent the first Each radar node The target distance, azimuth angle, and elevation angle information in the measurement matrix at any given time. Indicates in The target's motion state at any given time. Indicates in At this moment, the first The position coordinates of each radar node in the Cartesian coordinate system Indicates in The position coordinates of the target in the Cartesian coordinate system at that moment.
4. The target tracking method according to claim 3, characterized in that, The motion state of the target is represented as follows: ； In the above formula, The motion state vector of the target is represented in the following form: ,in, , , They represent the first in the Cartesian coordinate system. Position, velocity, and acceleration at any given moment Represents the state transition matrix. This represents Gaussian white noise with zero mean.
5. The target tracking method according to claim 3, characterized in that, All the filtered data are sequentially fused using the covariance cross-fusion algorithm. The fused target state estimate and its corresponding covariance representation are as follows: ； In the above formula, express At any given moment, the set of all tracking radar nodes of the target. This represents the fusion estimation state of space targets. The fusion covariance of the tracking is represented. This represents the weight of different radar nodes during the fusion process.
6. The target tracking method according to claim 5, characterized in that, The weights of different radar nodes in the fusion process are obtained using the following formula: 。 7. A target tracking system with multi-coverage fusion of space-based networked radar, characterized in that, The system includes: multiple space-based radars deployed on various orbital planes in low Earth orbit, and an information fusion unit; Each of the aforementioned space-based radars is used to observe target echo data from the same target. After preprocessing the target echo data, radar measurement data is obtained. This target measurement data is three-dimensional measurement data in a Cartesian coordinate system with the target as the origin. Unscented Kalman filtering is applied to the three-dimensional measurement data to obtain corresponding filtered data, which is then sent to the information fusion unit. Each of the aforementioned space-based radars is networked using a Walker constellation geometric configuration. The radar power-aperture product of each of the aforementioned space-based radars is obtained based on the dual-track element (TEL) information. The space-based radar power-aperture product guided by the dual-track element (TEL) information is expressed as: ； In the above formula, Indicates the signal-to-noise ratio per wavelet. This represents Boltzmann's constant. Indicates radar noise temperature. Represents the noise figure. Indicates system loss. Indicates the target scattering cross section. R Indicates the radar detection range. This indicates the TLE prediction error. Indicates the radar scanning solid angle Required scan time; The information fusion unit is used to sequentially fuse all the filtered data according to the covariance cross-fusion algorithm to obtain the target fusion state estimate, so as to achieve target tracking.