基于序贯蒙特卡洛与概率假设密度滤波的主动分布式声纳多目标跟踪

doi:10.3969/j.issn.1000-1093.2020.05.013

兵工学报 ›› 2020, Vol. 41 ›› Issue (5): 941-949.doi: 10.3969/j.issn.1000-1093.2020.05.013

基于序贯蒙特卡洛与概率假设密度滤波的主动分布式声纳多目标跟踪

邵鹏飞，王蕾，王方勇

（杭州应用声学研究所声纳技术重点实验室，浙江杭州 310012）

收稿日期:2019-05-15 修回日期:2019-05-15 上线日期:2020-07-17
作者简介:邵鹏飞(1989—),男,工程师。E-mail: 570384435@qq.com
基金资助:
国家自然科学基金项目（61701450）

Active Distributed Sonar Multi-target Tracking Based on SMC-PHD Filtering

SHAO Pengfei, WANG Lei, WANG Fangyong

(Science and Technology on Sonar Laboratory, Hangzhou Applied Acoustics Research Institute, Hangzhou 310012, Zhejiang, China)

Received:2019-05-15 Revised:2019-05-15 Online:2020-07-17

摘要/Abstract

摘要： 针对杂波数量多、目标数量和状态不确实性及观测不确实性等问题，提出了一种基于序贯蒙特卡洛与概率假设密度(SMC-PHD)滤波的分布式声纳多目标自动跟踪方法。通过随机有限集模型对多目标状态和观察进行表征，结合序贯蒙特卡洛方法中的重要性采样和重采样策略递归地实现多目标后验近似下概率假设密度的传递和滤波。利用分布式声纳观测模拟数据，对不同节点数目下基于SMC-PHD滤波的多目标跟踪进行了仿真实验。仿真实验结果表明：该方法适用于主动分布式声纳系统，能在多杂波环境下对数目未知且时变的多目标进行实时自动跟踪；在4个平台节点的主动分布式声纳系统中，实现了平均相对误差小于5%的水下多目标高精度跟踪，且目标数目估计值与真实值一致。

关键词: 主动分布式声纳, 随机有限集, 序贯蒙特卡洛, 概率假设密度滤波, 多目标跟踪

Abstract: An active distributed sonar multi-target automatic tracking method based on sequential Monte Carlo and probability hypothesis density (SMC-PHD) filtering is proposed to solve the problems of large number of clutter, targets uncertainty and observation uncertainty. In the proosed method, the random finite set (RFS) model is used to characterize the target state and observation, and the importance sampling and resampling strategy of sequential Monte Carlo (SMC) method is used to realize the transferring and filtering of probability hypothesis density of multi-target posterior. The multi-target tracking based on SMC-PHD filtering with different number of observation nodes is simulated. The results show that the proposed method can be used to effectively realize multi-target automatic tracking in real time in clutter environment with unknown and time-varying multi-targets. In active distributed sonar system with 4 nodes,the proposed method achieves the high-accuracy tracking with distance estimation relative error less than 0.05 and the completely accurate estimation of targets number. Key

Key words: activedistributedsonar, randomfiniteset, sequentialMonteCarlo, probabilityhypothesisdensityfiltering, multi-targettracking

中图分类号:

TB566

邵鹏飞，王蕾，王方勇. 基于序贯蒙特卡洛与概率假设密度滤波的主动分布式声纳多目标跟踪[J]. 兵工学报, 2020, 41(5): 941-949.

SHAO Pengfei, WANG Lei, WANG Fangyong. Active Distributed Sonar Multi-target Tracking Based on SMC-PHD Filtering[J]. Acta Armamentarii, 2020, 41(5): 941-949.

参考文献

［1］关键, 何友, 彭应宁. 多传感器分布式检测综述［J］.系统工程与电子技术, 2000, 22(12):11-15, 65.
GUAN J, HE Y,PENG Y N. Survey of distributed detection with multisensor［J］. Systems Engineering and Electronics, 2000, 22(12): 11-15, 65.(in Chinese)
［2］ BAR-SHALOM Y, FORTMANN T E. Tracking and data association［M］. Burlington, MA, US: Academic Press, 1987.
［3］ BLACKMAN S S. Multiple target tracking with radar applications［M］. Norwood, MA, US:Artech House, 1986.
［4］ BLACKMAN S S, POPPLI R. Design and analysis of modern tracking systems［M］. Norwood, MA, US:Artech House, 1999.
［5］ SINGER R A, STEIN J J. An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance system［C］∥Proceedings of the 10th IEEE Conference on Decision and Control. Miami Beach, FL, US:IEEE, 1971:171-175.
［6］倪龙强, 高社生, 薛丽. 低检测概率条件下的多传感器机动多目标跟踪方法研究［J］. 兵工学报, 2013, 34(1):87-92.
NI L Q, GAO S S, XUE L. A tracking method of multi-sensor to track the multiple targets under the condition of low detection pro-bability［J］. Acta Armamentarii, 2013, 34(1):87-92. (in Chinese)
［7］张俊根, 姬红兵, 蔡绍晓. 基于高斯粒子JPDA滤波的多目标跟踪算法［J］. 电子与信息学报, 2010, 32(11):2686-2690.
ZHANG J G, JI H B, CAI S X. Gaussian particle JPDA filter based multi-target tracking［J］. Journal of Electronics & Information Technology, 2010, 32(11):2686-2690.(in Chinese)
［8］ FORTMAN T E,SHALOM Y B,SCHEFFE M.Sonar tracking of multiple targets using joint probabilistic data association［J］. IEEE Journal of Oceanic Engineering, 1983, 8(3):173-184.
［9］ BLACKMAN S S. Multiple hypothesis tracking for multitarget tracking［J］. IEEE Transactions on Aerospace and Electronic Systems, 2004, 19(1):5-18.

［10］ VO B N, SINGH S, DOUCET A. Sequential Monte Carlo implementation of the PHD filter for multi-target tracking［C］∥Proceedings of the 6th International Conference on Information Fusion. Cairns, Queensland, Australia: IEEE, 2003:792-799.
［11］ VO B N, SINGH S,DOUCET A. Sequential Monte Carlo methods for multi-target filtering with random finite sets［J］. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224-1245.
［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］ FENG P M,WANG W W, NAQVI S M, et al. Adaptive retrodiction particle PHD filter for multiple human tracking［J］. IEEE Signal Processing Letters, 2016, 23(11):1592-1596.
［14］ KILIC V, BARNARD M, WANG W W, et al. Mean-shift and sparse sampling-based SMC-PHD filtering for audio informed visual speaker tracking［J］. IEEE Transactions on Multimedia, 2016, 18(12):2417-2431.
［15］ LI T C, ELVIRA V, FAN H Q, et al. Local-diffusion-based distributed SMC-PHD filtering using sensors with limited sensing range［J］. IEEE Sensors Journal, 2019, 19(4):1580-1589.
［16］史玺. 基于PHD滤波器的多目标跟踪方法研究［D］. 西安:西北工业大学, 2015.
SHI X. Research on multi-target tracking based on PHD Filter［D］. Xi'an:Northwestern Polytechnical University, 2015.(in Chinese)
［17］龙建乾. 基于FISST理论的多目标跟踪技术研究［D］. 长沙:国防科学技术大学, 2012.
LONG J Q.Research on the technology for multi-target tracking based on FISST theory［D］. Changsha:National University of Defense Technology, 2012.(in Chinese)
［18］周波. 基于随机有限集的多目标跟踪技术研究［D］. 广州:华南理工大学, 2013.
ZHOU B. Research on the technology for multi-target tracking based on random finite set［D］. Guangzhou:South China University of Technology, 2013.(in Chinese)
［19］欧阳成, 陈晓旭, 华云. 改进的最适高斯近似概率假设密度滤波［J］. 雷达学报, 2013, 2(2):239-246.
OUYANG C, CHEN X X, HUA Y. Improved best-fitting gaussian approximation PHD filter［J］. Journal of Radars, 2013, 2(2): 239-246.(in Chinese)
［20］ MAHLER R. Multi-target Bayes filtering via first-order multi-target moments［J］. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4):1152-1178.

第41卷第5期2020 年5月
兵工学报ACTA ARMAMENTARII
Vol.41No.5May2020

[1]	邹汝平，刘建书. 基于概率假设密度滤波与无迹Kalman滤波的多目标跟踪与识别[J]. 兵工学报, 2020, 41(8): 1502-1508.
[2]	魏帅, 冯新喜, 王泉, 鹿传国. 基于箱粒子滤波的鲁棒标签多伯努利跟踪算法[J]. 兵工学报, 2017, 38(10): 2062-2068.
[3]	李辉，张安，赵敏，沈莹. 两点式数据关联算法在多目标跟踪中的应用[J]. 兵工学报, 2007, 28(5): 633-637.

基于序贯蒙特卡洛与概率假设密度滤波的主动分布式声纳多目标跟踪

Active Distributed Sonar Multi-target Tracking Based on SMC-PHD Filtering

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价