[1]TANG Mengqi,LI Bo,HAO Lijun.Grid clustering measurement set partition method for extended target tracking[J].CAAI Transactions on Intelligent Systems,2022,17(4):806-813.[doi:10.11992/tis.202109013]
Copy

Grid clustering measurement set partition method for extended target tracking

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 4 Page number: 806-813 Column: 学术论文—机器感知与模式识别 Public date: 2022-07-05
Title:
Grid clustering measurement set partition method for extended target tracking
Author(s):
TANG Mengqi;  LI Bo;  HAO Lijun
School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China
Keywords:
extended target; measurement set; grid clustering; time-space correlation; fuzzy C-mean; survival target; newborn target; probability hypothesis density
CLC:
TN713;TP39
DOI:
10.11992/tis.202109013
Abstract:
To address the issues of difficult measurement set partitioning and inaccurate estimation of the number of targets in extended target tracking, we suggest a grid clustering measurement set partitioning approach for extended target tracking. Firstly, the current moment measurement is classified into two categories based on the time-space correlation between the targets: survival-target measurement and newborn-target measurement. Then, an improved fuzzy C-means algorithm and an improved grid clustering algorithm are derived for the Gaussian mixture probability hypothesis density filter and the extended target Gaussian mixture probability hypothesis density filter, respectively, which are employed to separate the viable target set and the new target set. The simulation results show that the proposed techniques can accurately divide the measurement set, effectively complete the extended target tracking, and avoid the missed and over-checked measurements.
References:
[1] 王雪, 李鸿艳, 孔云波, 等. 基于星凸RHM的扩展目标SMC-PHD滤波[J]. 计算机应用研究, 2017, 34(7): 2144–2147
WANG Xue, LI Hongyan, KONG Yunbo, et al. SMC-PHD filter for extended target tracking based on star-convex random hypersurface models[J]. Application research of computers, 2017, 34(7): 2144–2147
[2] LI Zhifei, ZHANG Jianyun, WANG Jiegui, et al. Extended object tracking using random matrix with converted measurements[J]. IET radar, sonar & navigation, 2020, 14(7): 981–991.
[3] 范建德, 谢维信. 一种高效的分布式多传感器多目标跟踪算法[J]. 信号处理, 2021, 37(3): 390–398
FAN Jiande, XIE Weixin. An efficient distributed multi-sensor multi-target tracking algorithm[J]. Journal of signal processing, 2021, 37(3): 390–398
[4] 李波, 昝珊珊. 基于自适应遗传PHD滤波的多群目标跟踪方法[J]. 计算机应用研究, 2018, 35(11): 3466–3468,3496
LI Bo, ZAN Shanshan. Multi-group-target tracking approach based on adaptive genetic PHD filter[J]. Application research of computers, 2018, 35(11): 3466–3468,3496
[5] LI Tiancheng, DE LA PRIETA PINTADO F, CORCHADO J M, et al. Multi-source homogeneous data clustering for multi-target detection from cluttered background with misdetection[J]. Applied soft computing, 2017, 60: 436–446.
[6] KUMRU M, K?KSAL H, ?ZKAN E. Variational measurement update for extended object tracking using Gaussian processes[J]. IEEE signal processing letters, 2021, 28: 538–542.
[7] 龚轩, 乐孜纯, 王慧, 等. 多目标跟踪中的数据关联技术综述[J]. 计算机科学, 2020, 47(10): 136–144
GONG Xuan, YUE Zichun, WANG Hui, et al. Survey of data association technology in multi-target tracking[J]. Computer science, 2020, 47(10): 136–144
[8] WIENEKE M, KOCH W. Probabilistic tracking of multiple extended targets using random matrices[C]//SPIE Defense, Security, and Sensing. Orlando: SPIE, 2010, 7698: 416?427.
[9] BAUM M, HANEBECK U D. Shape tracking of extended objects and group targets with star-convex RHMs[C]//14th International Conference on Information Fusion. Chicago: IEEE, 2011: 1?8.
[10] MAHLER R. Random sets: unification and computation for information fusion a retrospective assessment[C]//Proceedings of the 7th International Conference on Information Fusion. Stockholm: Information Fusion, 2004: 1?20.
[11] 张俊根. 一种粒子势概率假设密度滤波纯方位多目标跟踪算法[J]. 控制理论与应用, 2020, 37(6): 1319–1325
ZHANG Jungen. A particle cardinalized probability hypothesis density filtering algorithm for bearings-only multi-target tracking[J]. Control theory & applications, 2020, 37(6): 1319–1325
[12] VO B N, MA W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE transactions on signal processing, 2006, 54(11): 4091–4104.
[13] MAHLER R. PHD filters of higher order in target number[J]. IEEE transactions on aerospace and electronic systems, 2007, 43(4): 1523–1543.
[14] ZHANG Le, LAN Jian. Tracking of extended object using random matrix with non-uniformly distributed measurements[J]. IEEE transactions on signal processing, 2021, 69: 3812–3825.
[15] GRANSTROM K, ORGUNER U. A phd filter for tracking multiple extended targets using random matrices[J]. IEEE transactions on signal processing, 2012, 60(11): 5657–5671.
[16] 王子龙, 李进, 宋亚飞. 基于距离和权重改进的K-means算法[J]. 计算机工程与应用, 2020, 56(23): 87–94
WANG Zilong, LI Jin, SONG Yafei. Improved K-means algorithm based on distance and weight[J]. Computer engineering and applications, 2020, 56(23): 87–94
[17] TSAI C F, SUNG C Y. EIDBSCAN: an extended improving DBSCAN algorithm with sampling techniques[J]. International journal of business intelligence and data mining, 2010, 5(1): 94.
[18] 胡忠旺, 丁勇, 杨勇, 等. 基于时空关联—网格聚类的多扩展目标跟踪算法[J]. 传感器与微系统, 2019, 38(2): 129–132
HU Zhongwang, DING Yong, YANG Yong, et al. Multiple extended target tracking algorithm based on spatiotemporal correlation and grid clustering[J]. Transducer and microsystem technologies, 2019, 38(2): 129–132
[19] 何熊熊, 管俊轶, 叶宣佐, 等. 一种基于密度和网格的簇心可确定聚类算法[J]. 控制与决策, 2017, 32(5): 913–919
HE Xiongxiong, GUAN Junyi, YE Xuanzuo, et al. A density-based and grid-based cluster centers determination clustering algorithm[J]. Control and decision, 2017, 32(5): 913–919
[20] 欧阳城添, 汤懿. 基于高斯混合模型的核相关滤波目标跟踪算法[J]. 计算机工程与设计, 2019, 40(11): 3170–3174,3179
OUYANG Chengtian, TANG Yi. Kernelized correlation filters target tracking based on Gaussian mixed model[J]. Computer engineering and design, 2019, 40(11): 3170–3174,3179
[21] 曹倬, 冯新喜, 蒲磊, 等. 基于高斯混合概率假设密度滤波器的扩展目标跟踪算法[J]. 系统工程与电子技术, 2017, 39(3): 494–499
CAO Zhuo, FENG Xinxi, PU Lei, et al. Extended targets tracking algorithm based on Gaussian-mixture probability hypothesis density filter[J]. Systems engineering and electronics, 2017, 39(3): 494–499
[22] YAO Gang, SALTUS R, DANI A. Image moment-based extended object tracking for complex motions[J]. IEEE sensors journal, 2020, 20(12): 6560–6572.
[23] LYU Xuebin, ZHOU Qunbiao, CHEN Zhengmao, et al. The Gaussian mixture probability hypothesis density filter and its application to multi-target tracking[J]. Chinese journal of computers, 2012, 35(2): 397–404.
[24] 蒋莉芳, 苏一丹, 覃华. 迭代吉洪诺夫正则化的FCM聚类算法[J]. 计算机工程与设计, 2017, 38(9): 2391–2395
JIANG Lifang, SU Yidan, QIN Hua. Fuzzy C-means clustering based on iterative Tikhonov regularization[J]. Computer engineering and design, 2017, 38(9): 2391–2395
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems