A Multi-sensor Adaptive Observation Iteratively Updating GM-PHD Tracking Algorithm

SHENTU Han; LI Kaibin; RONG Yingjiao; LI Yanxin; GUO Yunfei

doi:10.11999/JEIT211138

Volume 44 Issue 12

Dec. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(12): 4168-4177

SHENTU Han, LI Kaibin, RONG Yingjiao, LI Yanxin, GUO Yunfei. A Multi-sensor Adaptive Observation Iteratively Updating GM-PHD Tracking Algorithm[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4168-4177. doi: 10.11999/JEIT211138

Citation:

SHENTU Han, LI Kaibin, RONG Yingjiao, LI Yanxin, GUO Yunfei. A Multi-sensor Adaptive Observation Iteratively Updating GM-PHD Tracking Algorithm[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4168-4177. doi: 10.11999/JEIT211138

Citation:

PDF( 7704 KB)

A Multi-sensor Adaptive Observation Iteratively Updating GM-PHD Tracking Algorithm

doi: 10.11999/JEIT211138

1.
Institution of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China

Funds: The Technology Foundation for Basic Enhancement Plan (2021-JCJQ-JJ-0301), The Foundation of Key Laboratory of Near Ground Detection Technology (6142414200203), The Fundamental Research Funds for the Provincial Universities of Zhejiang (GK219909299001-405), Zhejiang Provincial Natural Science Foundation of China (LZ20F010002), The National College Students Innovation and Entrepreneurship Training Program of China (202110336022)

Received Date: 2021-10-18
Accepted Date: 2022-05-24
Rev Recd Date: 2022-05-18

Available Online: 2022-05-30

Publish Date: 2022-12-16

Abstract

Abstract

For the problem that the results of multi-sensor measurement iteratively updating Gaussian Mixture Probability Hypothesis Density (GM-PHD) tracking algorithm is sensitive to the updating order if the qualities of multi-sensor observation data are different and unknown, a multi-sensor Adaptive observation Iteratively Updating GM-PHD tracking algorithm (AIU-GM-PHD) is proposed. Firstly, based on the multi-sensor fusion consistency measure, a method is proposed to evaluate the online quality of each sensor's tracking results. Then, the sequence of multi-sensor iterative fusion is optimized. Finally, the corresponding multi-sensor GM-PHD fusion tracking algorithm is constructed. To solve the problem that the multi-sensor adaptive order iterative fusion can not reflect the sensor quality gap, an Adaptive Iteratively Updating GM-PHD tracking algorithm PAIU-GM-PHD with weighted pseudo measurements is proposed. The simulation results show that, compared with the conventional multi-sensor iterative update GM-PHD tracking algorithm, the proposed algorithms can obtain more robust and accurate tracking results.

FullText(HTML)

References(28)

References

[1]	杨威, 付耀文, 龙建乾, 等. 基于有限集统计学理论的目标跟踪技术研究综述[J]. 电子学报, 2012, 40(7): 1440–1448. doi: 10.3969/j.issn.0372-2112.2012.07.025 YANG Wei, FU Yaowen, LONG Jianqian, et al. Research review of target tracking technology based on the theory of finite set statistics[J]. Acta Electronica Sinica, 2012, 40(7): 1440–1448. doi: 10.3969/j.issn.0372-2112.2012.07.025
[2]	黄静琪, 胡琛, 孙山鹏, 等. 一种基于异步传感器网络的空间目标分布式跟踪方法[J]. 电子与信息学报, 2020, 42(5): 1132–1139. doi: 10.11999/JEIT190460 HUANG Jingqi, HU Chen, SUN Shanpeng, et al. A distributed space target tracking algorithm based on asynchronous multi-sensor networks[J]. Journal of Electronics &Information Technology, 2020, 42(5): 1132–1139. doi: 10.11999/JEIT190460
[3]	MA Ke, ZHANG Hanguang, WANG Rentao, et al. Target tracking system for multi-sensor data fusion[C]. The 2nd Information Technology, Networking, Electronic and Automation Control Conference, Chengdu, China, 2017: 1768–1772.
[4]	LIANG Shuang, ZHU Yun, LI Hao, et al. Nearest-neighbour joint probabilistic data association filter based on random finite set[C]. 2019 International Conference on Control, Automation and Information Sciences, Chengdu, China, 2019: 1–6.
[5]	BAR-SHALOM Y and TSE E. Tracking in a cluttered environment with probabilistic data association[J]. Automatica, 1975, 11(5): 451–460. doi: 10.1016/0005-1098(75)90021-7
[6]	VENUS A, LEITINGER E, TERTINEK S, et al. A message passing based adaptive PDA algorithm for robust radio-based localization and tracking[C]. 2021 IEEE Radar Conference (RadarConf21), Atlanta, USA, 2021: 1–6.
[7]	ANGLE R B, STREIT R L, and EFE M. A low computational complexity JPDA filter with superposition[J]. IEEE Signal Processing Letters, 2021, 28(1): 1031–1035. doi: 10.1109/LSP.2021.3082040
[8]	BAR-SHALOM Y, FORTMANN T E, and CABLE P G. Tracking and data association[J]. The Journal of the Acoustical Society of America, 1990, 87(2): 918–919. doi: 10.1121/1.398863
[9]	CARTHEL C, LENOACH J, CORALUPPI S, et al. Analysis of MHT and GBT approaches to disparate-sensor fusion[C]. The IEEE 23rd International Conference on Information Fusion, Rustenburg, South Africa, 2020: 1–7.
[10]	MORI S, CHONG C Y, WISHNER R P, et al. Multitarget multi sensor tracking problems: A general Bayesian approach[C]. 1983 American Control Conference, San Francisco, USA, 1983: 452–457.
[11]	LEUNG K Y K, INOSTROZA F, and ADAMS M. Relating random vector and random finite set estimation in navigation, mapping, and tracking[J]. IEEE Transactions on Signal Processing, 2017, 65(17): 4609–4623. doi: 10.1109/TSP.2017.2701330
[12]	SONG Yan, HU Jianwang, and JI Bing. A survey of PHD filtering method based on random finite set[C]. The 2nd International Conference on Mechatronics Engineering and Information Technology, Dalian, China, 2017: 199–204.
[13]	GARCÍA-FERNÁNDEZ Á F and MASKELL S. Continuous-discrete trajectory PHD and CPHD filters[C]. The IEEE 23rd International Conference on Information Fusion, Rustenburg, South Africa, 2020: 1–8.
[14]	CHOI M E and SEO S W. Robust multitarget tracking scheme based on Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Vehicular Technology, 2016, 65(6): 4217–4229. doi: 10.1109/TVT.2015.2479363
[15]	GU Zihao, WANG Ping, and LIU Fuqiang. Cooperative vehicle tracking using SMC-PHD integrated with interacting multiple models[C]. 2020 IEEE MTT-S International Wireless Symposium, Shanghai, China, 2020: 1–3.
[16]	MA W K, VO B N, SINGH S S, et al. Tracking an unknown time-varying number of speakers using TDOA measurements: A random finite set approach[J]. IEEE Transactions on Signal Processing, 2006, 54(9): 3291–3304. doi: 10.1109/TSP.2006.877658
[17]	DA Kai, LI Tiancheng, ZHU Yongfeng, et al. Recent advances in multisensor multitarget tracking using random finite set[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 5–24. doi: 10.1631/FITEE.2000266
[18]	XU Jian, HUANG Fangming, and HUANG Zhilinag. The multi-sensor PHD filter: Analytic implementation via Gaussian mixture and effective binary partition[C]. The 16th International Conference on Information Fusion, Istanbul, Turkey, 2013: 945–952.
[19]	熊伟, 顾祥岐, 徐从安, 等. 多编队目标先后出现时的无先验信息跟踪方法[J]. 电子与信息学报, 2020, 42(7): 1619–1626. doi: 10.11999/JEIT190508 XIONG Wei, GU Xiangqi, XU Congan, et al. Tracking method without prior information when multi-group targets appear successively[J]. Journal of Electronics &Information Technology, 2020, 42(7): 1619–1626. doi: 10.11999/JEIT190508
[20]	MEYER F, KROPFREITER T, WILLIAMS J L, et al. Message passing algorithms for scalable multitarget tracking[J]. Proceedings of the IEEE, 2018, 106(2): 221–259. doi: 10.1109/JPROC.2018.2789427
[21]	HAN Shentu, QIAN Hanming, PENG Dongliang, et al. An unbalanced weighted sequential fusing multi-sensor GM-PHD algorithm[J]. Sensors, 2019, 19(2): 366. doi: 10.3390/s19020366
[22]	NAGAPPA S and CLARK D E. On the ordering of the sensors in the iterated-corrector probability hypothesis density (PHD) filter[C]. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, Orlando, USA, 2011: 80500M.
[23]	LIU Long, JI Hongbing, and FAN Zhenhua. Improved iterated-corrector PHD with Gaussian mixture implementation[J]. Signal Processing, 2015, 114: 89–99. doi: 10.1016/j.sigpro.2015.01.007
[24]	LIU Long, JI Hongbing, ZHANG Wenbo, et al. Multi-sensor fusion for multi-target tracking using measurement division[J]. IET Radar, Sonar & Navigation, 2020, 14(9): 1451–1461. doi: 10.1049/iet-rsn.2018.5567
[25]	王宝树, 李芳社. 基于数据融合技术的多目标跟踪算法研究[J]. 西安电子科技大学学报, 1998, 25(3): 269–272. doi: 10.3969/j.issn.1001-2400.1998.03.001 WANG Baoshu and LI Fangshe. The research on multiple targets tracking based on the data fusion technique[J]. Journal of Xidian University, 1998, 25(3): 269–272. doi: 10.3969/j.issn.1001-2400.1998.03.001
[26]	MAHLER R. Approximate multi sensor CPHD and PHD filters[C]. The 13th International Conference on Information Fusion, Edinburgh, UK, 2010: 1–8.
[27]	BEARD M, VO B T, and VO B N. OSPA⁽²⁾: Using the OSPA metric to evaluate multi-target tracking performance[C]. 2017 International Conference on Control, Automation and Information Sciences, Chiang Mai, Thailand, 2017: 86–91.
[28]	STREIT R L. Poisson Point Process: Imaging, Tracking, and Sensing[M]. New York: Springer, 2010: 273–280.