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| Citation: | WANG Zengfu, SHAO Yi, QI Dengliang, et al. Consistency-based air target height estimation and location in distributed space-based radar network[J]. Journal of Radars, 2023, 12(6): 1249–1262. doi: 10.12000/JR23157
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Consistency-based Air Target Height Estimation and Location in Distributed Space-based Radar Network
DOI: 10.12000/JR23157
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Abstract
When single space-based radar tracks and detects air targets, problems such as missing pitch angle information and nonlinear measurement lead to large target height estimation errors. Multi-space-based radar networking can solve this problem. Moreover, considering the system’s requirements for low computational complexity, low communication overhead, high accuracy, and high reliability, a consistency-based method for height estimation and location of air targets in a distributed space-based radar network is proposed. First, an air target motion model and a space-based radar measurement model are presented. Second, based on probabilistic graphical model theory, a factor graph for multi-frame measurement of target tracking and positioning in a space-based radar network is established. The coupling relationship between several local target motion states is established based on consistency fusion. Third, combining particle filtering and belief propagation establishes the message representation and iterative calculation rules of nonparametric belief propagation on the fusion tracking for factor graph of space-based radar networking. Finally, the performance of the algorithm is tested through simulation. The simulation results show that compared with the distributed consensus extended Kalman filter, the proposed algorithm improves the target height estimation accuracy by 35.3%, effectively improving the target localization performance of the space-based radar. -
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References
[1] GRAVES R H W. Detection of airborne targets by a space-based radar using multipath interference[C]. 1991 IEEE National Radar Conference, Los Angeles, USA, 1991: 46–49.[2] KULIKOV G Y and KULIKOVA M V. The accurate continuous-discrete extended Kalman filter for radar tracking[J]. IEEE Transactions on Signal Processing, 2016, 64(4): 948–958. doi: 10.1109/TSP.2015.2493985[3] KULIKOV G Y and KULIKOVA M V. Square-root accurate continuous-discrete extended-unscented Kalman filtering methods with embedded orthogonal and J-orthogonal QR decompositions for estimation of nonlinear continuous-time stochastic models in radar tracking[J]. Signal Processing, 2020, 166: 107253. doi: 10.1016/j.sigpro.2019.107253[4] ZHANG Qian and SONG T L. Gaussian mixture presentation of measurements for long-range radar tracking[J]. Digital Signal Processing, 2016, 56: 110–122. doi: 10.1016/j.dsp.2016.06.008[5] 张连仲, 王宝宝, 王超尘. 一种基于期望最大化去偏转换量测滤波的目标跟踪算法[J]. 中国惯性技术学报, 2020, 28(6): 829–833. doi: 10.13695/j.cnki.12-1222/o3.2020.06.020ZHANG Lianzhong, WANG Baobao, and WANG Chaochen. A target tracking algorithm based on expectation maximization debiased conversion measurement filter[J]. Journal of Chinese Inertial Technology, 2020, 28(6): 829–833. doi: 10.13695/j.cnki.12-1222/o3.2020.06.020[6] WU Qisong, CHEN Lingjie, LI Yanping, et al. Reweighted robust particle filtering approach for target tracking in automotive radar application[J]. Remote Sensing, 2022, 14(21): 5477. doi: 10.3390/rs14215477[7] AIT-EL-FQUIH B and HOTEIT I. A variational Bayesian multiple particle filtering scheme for large-dimensional systems[J]. IEEE Transactions on Signal Processing, 2016, 64(20): 5409–5422. doi: 10.1109/TSP.2016.2580524[8] 闫文旭, 兰华, 王增福, 等. 基于变分贝叶斯的星载雷达非线性滤波[J]. 航空学报, 2020, 41(S2): 724395. doi: 10.7527/S1000-6893.2020.24395YAN Wenxu, LAN Hua, WANG Zengfu, et al. Nonlinear filtering for spaceborne radars based on variational Bayes[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(S2): 724395. doi: 10.7527/S1000-6893.2020.24395[9] LAN Hua, MA Jirong, WANG Zengfu, et al. A message passing approach for multiple maneuvering target tracking[J]. Signal Processing, 2020, 174: 107621. doi: 10.1016/j.sigpro.2020.107621[10] GUO Zhen, WANG Zengfu, LAN Hua, et al. OTHR multitarget tracking with a GMRF model of ionospheric parameters[J]. Signal Processing, 2021, 182: 107940. doi: 10.1016/j.sigpro.2020.107940[11] XU Jing, YANG Gongliu, SUN Yiding, et al. A multi-sensor information fusion method based on factor graph for integrated navigation system[J]. IEEE Access, 2021, 9: 12044–12054. doi: 10.1109/ACCESS.2021.3051715[12] YU Zehua, LI Jun, GUO Qinghua, et al. Efficient direct target localization for distributed MIMO radar with expectation propagation and belief propagation[J]. IEEE Transactions on Signal Processing, 2021, 69: 4055–4068. doi: 10.1109/TSP.2021.3092363[13] DESINGH K, LU Shiyang, OPIPARI A, et al. Efficient nonparametric belief propagation for pose estimation and manipulation of articulated objects[J]. Science Robotics, 2019, 4(30): eaaw4523. doi: 10.1126/scirobotics.aaw4523[14] 郭振, 王增福, 白向龙, 等. 消息传递方法及其在信息融合中的应用[J]. 控制与决策, 2022, 37(10): 2443–2455. doi: 10.13195/j.kzyjc.2021.0367GUO Zhen, WANG Zengfu, BAI Xianglong, et al. Message passing methods and their applications in information fusion[J]. Control and Decision, 2022, 37(10): 2443–2455. doi: 10.13195/j.kzyjc.2021.0367[15] LAN Hua, WANG Zengfu, BAI Xianglong, et al. Measurement-level target tracking fusion for over-the-horizon radar network using message passing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(3): 1600–1623. doi: 10.1109/TAES.2020.3044109[16] WU Yuanxin, WANG Ping, and HU Xiaoping. Algorithm of earth-centered earth-fixed coordinates to geodetic coordinates[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1457–1461. doi: 10.1109/TAES.2003.1261144[17] WANG Shengdi and DEKORSY A. A factor graph-based distributed consensus Kalman filter[J]. IEEE Signal Processing Letters, 2020, 27: 2039–2043. doi: 10.1109/LSP.2020.3036337[18] LI Wei, YANG Zhen, and HU Haifeng. Sequential particle-based sum-product algorithm for distributed inference in wireless sensor networks[J]. IEEE Transactions on Vehicular Technology, 2013, 62(1): 341–348. doi: 10.1109/TVT.2012.2221484[19] CHAHROUR H, DANSEREAU R M, RAJAN S, et al. Target detection through Riemannian geometric approach with application to drone detection[J]. IEEE Access, 2021, 9: 123950–123963. doi: 10.1109/ACCESS.2021.3105594 -
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- Figure 1. Coordinate systems
- Figure 2. Procedure of coordinate systems transformation
- Figure 3. Space-based radar antenna array coordinate system
- Figure 4. Factor graph decomposition of the joint probability distribution function of target states and measurements
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Figure 5. A local factor graph model at time
$k$ -
Figure 6. The factor graph at time
$k$ - Figure 7. Block diagram of the distributed implementation of DCNBP
- Figure 8. RMSE of aerial target altitude estimation
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Figure 9. RMSE of aerial target altitude estimation (particle number
$S = 400$ -
Figure 10. RMSE of aerial target altitude estimation (particle number
$S = 800$ - Figure 11. RMSE of aerial target height estimation with different measurement errors
- Figure 12. RMSE of aerial target position estimation with different altitude errors