Multi-sensor Multiple Maneuvering Targets Tracking Algorithm under Greedy Measurement Partitioning Mechanism

Biao YANG; Shengqi ZHU; Kun YU; Yunfei FANG

doi:10.11999/JEIT200498

Volume 43 Issue 7

Jul. 2021

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Biao YANG, Shengqi ZHU, Kun YU, Yunfei FANG. Multi-sensor Multiple Maneuvering Targets Tracking Algorithm under Greedy Measurement Partitioning Mechanism[J]. Journal of Electronics & Information Technology, 2021, 43(7): 1962-1969. doi: 10.11999/JEIT200498

Citation:

Biao YANG, Shengqi ZHU, Kun YU, Yunfei FANG. Multi-sensor Multiple Maneuvering Targets Tracking Algorithm under Greedy Measurement Partitioning Mechanism[J]. Journal of Electronics & Information Technology, 2021, 43(7): 1962-1969. doi: 10.11999/JEIT200498

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Multi-sensor Multiple Maneuvering Targets Tracking Algorithm under Greedy Measurement Partitioning Mechanism

doi: 10.11999/JEIT200498

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710077, China

Funds: The National Natural Science Foundation of China(61771015)

Received Date: 2020-06-18
Rev Recd Date: 2021-01-15

Available Online: 2021-01-22

Publish Date: 2021-07-10

Abstract

Abstract

A novel method Interacting Multiple Mode Multi-Sensor Multi-target Multi-Bernoulli (IMM-MS-MeMBer) filter to track multiple maneuvering targets in low detection probability scenario is proposed. At the prediction stage of the IMM-MS-MeMBer filter, model probability of the target is adaptively updated by utilizing the current measurement information, and then the mixed prediction of the target state is executed; At the update stage of the IMM-MS-MeMBer filter, the greedy multi-sensor measurement partitioning strategy is employed in measurement partition step, the posterior probability density of the target is updated by using the divided set of measurements and the IMM-MS-MeMBer filter; In addition, the IMM-MS-MeMBer filter utilizes the target angle and Doppler information to realize the simultaneous estimation of the position and speed of multiple maneuvering targets. Numerical experiments verify the superior performance of the IMM-MS-MeMBer filter.
- Interactive multiple model,
- Maneuvering target,
- Multi-sensor,
- Multi-target multi-Bernoulli filtering,
- Greedy algorithm

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References(16)

References

[1]	MAHLER R P S. Statistical Multisource-Multitarget Information Fusion[M]. Boston: Artech House, 2007: 348–356, 660–668.
[2]	彭华甫, 黄高明, 田威. 随机有限集理论及其在多目标跟踪中的应用和实现[J]. 控制与决策, 2019, 34(2): 225–232. doi: 10.13195/j.kzyjc.2017.1326 PENG Huafu, HUANG Gaoming, and TIAN Wei. Random finite set: Theory, application and implementation for multi-target tracking[J]. Control and Decision, 2019, 34(2): 225–232. doi: 10.13195/j.kzyjc.2017.1326
[3]	陈辉, 韩崇昭. 机动多目标跟踪中的传感器控制策略的研究[J]. 自动化学报, 2016, 42(4): 512–523. doi: 10.16383/j.aas.2016.c150529 CHEN Hui and HAN Chongzhao. Sensor control strategy for maneuvering multi-target tracking[J]. Acta Automatica Sinica, 2016, 42(4): 512–523. doi: 10.16383/j.aas.2016.c150529
[4]	江同洋, 刘妹琴, 张森林. 非线性多模型多伯努利滤波器的实现[J]. 华中科技大学学报: 自然科学版, 2015, 43(9): 7–12, 18. doi: 10.13245/j.hust.150902 JIANG Tongyang, LIU Meiqin, and ZHANG Senlin. Implementation of multiple-model multi-Bernoulli filter for nonlinear models[J]. Journal of Huazhong University of Science and Technology:Natural Science Edition, 2015, 43(9): 7–12, 18. doi: 10.13245/j.hust.150902
[5]	杨峰, 张婉莹. 一种多模型贝努利粒子滤波机动目标跟踪算法[J]. 电子与信息学报, 2017, 39(3): 634–639. doi: 10.11999/JEIT160467 YANG Feng and ZHANG Wanying. Multiple model bernoulli particle filter for maneuvering target tracking[J]. Journal of Electronics &Information Technology, 2017, 39(3): 634–639. doi: 10.11999/JEIT160467
[6]	许红, 谢文冲, 袁华东, 等. 基于自适应的增广状态-交互式多模型的机动目标跟踪算法[J]. 电子与信息学报, 2020, 42(11): 2749–2755. doi: 10.11999/JEIT190516 XU Hong, XIE Wenchong, YUAN Huadong, et al. Maneuvering target tracking algorithm based on the adaptive augmented state interracting multiple model[J]. Journal of Electronics &Information Technology, 2020, 42(11): 2749–2755. doi: 10.11999/JEIT190516
[7]	夏小虎, 刘明. 联合约束级联交互式多模型滤波器及其在机动目标跟踪中的应用[J]. 电子与信息学报, 2017, 39(1): 117–123. doi: 10.11999/JEIT160384 XIA Xiaohu and LIU Ming. Unified constrained cascade interactive multi-model filter and its application in tracking of manoeuvring target[J]. Journal of Electronics &Information Technology, 2017, 39(1): 117–123. doi: 10.11999/JEIT160384
[8]	QIU Jing, XING Zirui, ZHU Chunsheng, et al. Centralized fusion based on interacting multiple model and adaptive Kalman filter for target tracking in underwater acoustic sensor networks[J]. IEEE Access, 2019, 7: 25948–25958. doi: 10.1109/ACCESS.2019.2899012
[9]	刘悄然, 杨训. 基于改进的交互式多模型粒子滤波算法[J]. 西北工业大学学报, 2018, 36(1): 169–175. doi: 10.1051/jnwpu/20183610169 LIU Qiaoran and YANG Xun. Improved interacting multiple model particle filter algorithm[J]. Journal of Northwestern Polytechnical University, 2018, 36(1): 169–175. doi: 10.1051/jnwpu/20183610169
[10]	MAHLER R. The multisensor PHD filter: I. General solution via multitarget calculus[C]. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, Orlando, USA, 2009: 73360E. doi: 10.1117/12.818024.
[11]	DELANDE E, DUFLOS E, HEURGUIER D, et al. Multi-target PHD filtering: Proposition of extensions to the multi-sensor case[R]. RR-7337. Lille, France: INRIA, 2010: 1–69.
[12]	TOBIAS M and LANTERMAN A D. Multitarget tracking using multiple bistatic range measurements with probability hypothesis densities[C]. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, Orlando, USA, 2004: 296–305. doi: 10.1117/12.544027.
[13]	BATTISTELLI G, CHISCI L, MORROCCHI S, et al. Robust multisensor multitarget tracker with application to passive multistatic radar tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3450–3472. doi: 10.1109/taes.2012.6324726
[14]	NANNURU S, BLOUIN S, COATES M, et al. Multisensor CPHD filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(4): 1834–1854. doi: 10.1109/TAES.2016.150265
[15]	SAUCAN A A, COATES M J, and RABBAT M. A multisensor multi-Bernoulli filter[J]. IEEE Transactions on Signal Processing, 2017, 65(20): 5495–5509. doi: 10.1109/TSP.2017.2723348
[16]	ARULAMPALAM M S, MASKELL S, GORDON N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Transactions on Signal Processing, 2002, 50(2): 174–188. doi: 10.1109/78.978374