一种基于GM-PHD滤波的纯方位多目标跟踪方法研究
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- 英文论文题名：A bearings-only multi-targets tracking method based on GM-PHD filtering
- 论文作者：朱宗斌 ; 陶剑锋 ; 葛辉良 ; 孟彧仟 ; 石超雄
- 英文论文作者：ZHU Zong-bin ; TAO Jian-feng ; GE Hui-liang ; MENG Yu-qian ; SHI Chao-xiong ; Hang Zhou Applied Acoustics Research Institute
- 年：2016
- 作者机构：杭州应用声学研究所;
- 论文关键词：被动声纳 ; 多目标纯方位跟踪 ; GM-PHD滤波 ; 非线性 ; 目标交叉
- 英文论文关键词：passive sonar ; bearings-only multi-targets tracking ; GM-PHD filter ; non-linear ; crossing targets
- 会议召开时间：2016-08-21
- 会议录名称：2016’中国西部声学学术交流会论文集
- 语种：中文
- 分类号：U666.7
- 学会代码：SXJS
- 会议名称：2016’中国西部声学学术交流会
- 会议地点：中国四川成都
- 主办单位：中国声学学会微声学分会、中国声学学会物理声学分会、陕西省声学学会、浙江省声学学会、山东省声学学会、上海市声学学会、黑龙江省声学学会、水声技术重点实验室、重庆市声学学会、西安市声学学会、四川省声学学会
- 学会名称：《声学技术》编辑部
- 页数：6
- 文件大小：1305k
- 原文格式：D
- 会议级别：全国
摘要
针对被动声纳多目标纯方位跟踪问题,研究了一种基于高斯混合概率假设密度(Gaussian Mixture Probability Hypothesis Density,GM-PHD)滤波的多目标方位跟踪方法。鉴于被动声纳观测信息的非线性,提出一种基于卡尔曼滤波(Kalman Filter,KF)预测及扩展卡尔曼滤波(Extended Kalman Filter,EKF)更新的GM-PHD滤波跟踪算法。通过建立目标状态和目标观测模型,利用多目标运动状态信息及双基地被动声纳方位观测信息,对多目标方位跟踪进行仿真,并将GM-PHD跟踪方法与传统跟踪方法的跟踪效果进行比较。结果表明,本文方法可在复杂杂波环境、目标新生及衍生和目标交叉情况下对多目标进行有效跟踪。
Aiming at bearings-only multi-targets tracking of passive sonar, this paper investigates a method based on the Gaussian mixture probability hypothesis density(GM-PHD) filter. Due to the non-linear characteristics of passive sonar's observation information, A tracking algorithm based on GM-PHD filter which predicting based on Kalman filter and updating based on extended Kalman filter is put forward. Through establishing target state and target observation model, using multi-targets state and Twin-Station passive sonar observation information, the multi-targets bearings tracking is simulated, and the effects of GM-PHD tracking method are compared with traditional tracking method. Simulation results show that the proposed method can effectively track multi-targets in situation with complex clutter, having birth, spawning and crossing targets.
Aiming at bearings-only multi-targets tracking of passive sonar, this paper investigates a method based on the Gaussian mixture probability hypothesis density(GM-PHD) filter. Due to the non-linear characteristics of passive sonar's observation information, A tracking algorithm based on GM-PHD filter which predicting based on Kalman filter and updating based on extended Kalman filter is put forward. Through establishing target state and target observation model, using multi-targets state and Twin-Station passive sonar observation information, the multi-targets bearings tracking is simulated, and the effects of GM-PHD tracking method are compared with traditional tracking method. Simulation results show that the proposed method can effectively track multi-targets in situation with complex clutter, having birth, spawning and crossing targets.
引文
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[2]Willett P,Luginbuhl T.MHT tracking for crossing sonar targets[C].SPIE conference on Signal and Data Processing of Small Targets,2007,6(2):66991C-66991C-12.
[3]Mahler R.Statistical multisource multitarget information fusion[M].Boston:Artech House,2007.
[4]Mahler R.Multitarget Bayes filtering via first-order multi-target moments[J].IEEE Transactions on Aerospace and Electronic Systems,2003,39(4):1152-1178.
[5]Vo B N,Singh S,Doucet A.Sequential Monte Carlo methods for multitarget filtering with random finite sets[J].IEEE Transactions on Aerospace and Electronic Systems,2005,41(4):1224-1245.
[6]Vo B N,Ma W K.The Gaussian mixture probability hypothesis density filter[J].IEEE Transactions on Signal Processing,2006,54(11):4091-4104.
[7]Kalyan B,Balasuriya A,Wijesoma S.Multiple targets tracking in underwater sonar images using particle-PHD filter[C].The 16th IEEE International Conference on ISAF,2006,1-5.
[8]Clark D,Vo B N,Bell J.GM-PHD filter multi-target tracking in sonar images[C].Defense and Security Symposium,2006,6235:62350R-62350R-8.
[9]Vo B T.Random finite sets in multi-object filtering[D].University of Western Australia,Ph D Thesis,2008.
[10]孟凡彬.基于随机集理论的多目标跟踪技术研究[D].哈尔滨:哈尔滨工程大学,2010.Meng F B.Research on the multi-target tracking techniques based on random set theory[D].Harbin:Harbin Engineering University,2010.
[11]Schuhmacher D,Vo B T,Vo B N.A Consistent metric for performance evaluation of multi-object filters[J].IEEE Transactions on Signal Processing,2008,56(8):3447-3457.