异步多传感器多目标PHD航迹合成算法

doi:10.7527/S1000-6893.2013.0338

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异步多传感器多目标PHD航迹合成算法

吴鑫辉^1,2, 黄高明¹, 高俊¹

1. 海军工程大学电子工程学院, 湖北武汉 430033;
2. 海军装备研究院指挥自动化所, 北京 100036

收稿日期:2012-12-25 修回日期:2013-07-11 出版日期:2013-12-25 发布日期:2013-09-05
通讯作者: 黄高明，Tel.：027-83444077E-mail：hgaom@163.com E-mail:hgaom@163.com
作者简介:吴鑫辉男，博士研究生。主要研究方向：数字信号处理、目标无源探测与跟踪。E-mail：wuxinhui009@163.com；黄高明男，教授，博士生导师。主要研究方向：雷达/电子战信号处理、盲信号处理、无源探测、电子战系统仿真与效能评估。Tel：027-83444077E-mail：hgaom@163.com；高俊男，教授，博士生导师。主要研究方向：数字信号处理、数字通信、短波无线通信。E-mail：gjhjgc@163.com
基金资助:
国家自然科学基金（60901069）

PHD for Composite Tracking Algorithm Based on Asynchronous Multi-sensor Multi-target Measurements

WU Xinhui^1,2, HUANG Gaoming¹, GAO Jun¹

1. College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China;
2. Automatization of Command Institute, Academy of Navy Equipment, Beijing 100036, China

Received:2012-12-25 Revised:2013-07-11 Online:2013-12-25 Published:2013-09-05

摘要/Abstract

摘要：

针对传统异步多传感器航迹合成算法存在计算量大及航迹丢失等问题，提出了一种基于概率假设密度（PHD）的多目标航迹合成算法。将监测区域划分为单传感器区域、多传感器交叉区域以及探测盲区3类。在随机集理论框架下，推导了3类区域的多传感器多目标PHD递推式，并给出了区域之间航迹初始和航迹维持方法。最后推导了线性高斯条件下各区域PHD航迹合成递推式的闭集解。仿真实例表明，相比乘积多传感器PHD算法，该算法能有效地减小计算量，并且能跟踪探测盲区中的目标，具有良好的工程应用前景。

关键词: 随机有限集, 多目标航迹合成, 多传感器多目标跟踪, 概率假设密度, 乘积多传感器PHD

Abstract:

The current composite tracking algorithms are computationally intractable and may lose target tracks in the undetected region. In order to solve these problems, a new composite tracking algorithm based on the probability hypothesis density (PHD) algorithm is proposed. The detection region is divided into a one-sensor region, a multiple-sensor region and an undetected region. Multi-sensor PHD filters for the regions are constructed using finite sets statistics theory (FISST). Tracking initiation and tracking maintenance methods for different regions are presented. Finally, the closed-form solutions to the PHD composite tracking algorithm are derived under the linear-Gaussian conditions. Compared with the product multi-sensor PHD, simulation results show that the proposed algorithm has lower computational complexity and better estimation of target states, which indicates its good prospect for application in engineering fields.

Key words: random finite set, multi-target composite tracking, multi-sensor multi-target tracking, probability hypothesis density, product multi-sensor PHD

中图分类号:

V243.2
TP391

吴鑫辉, 黄高明, 高俊. 异步多传感器多目标PHD航迹合成算法[J]. 航空学报, 2013, 34(12): 2785-2793.

WU Xinhui, HUANG Gaoming, GAO Jun. PHD for Composite Tracking Algorithm Based on Asynchronous Multi-sensor Multi-target Measurements[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(12): 2785-2793.

参考文献

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[2] Huang Y P, WU H B, Zhang Z Y. Bearing combination-based fusion algorithm for heterogeneous sensors track data. Journal of Southwest Jiaotong University, 2011, 46(2): 277-281. (in Chinese) 黄友彭, 吴汉宝, 张志云. 基于方位合成的异类传感器航迹数据融合算法. 西南交通大学学报, 2011, 46(2): 277-281.

[3] Vo B T. Random finite sets in multi-object filtering. Ph.D dissertation, The University of Western, Australia, 2008.

[4] Pollard E, Pannetier B, Romabaut M. Hybrid algorithms for multitarget tracking using MHT and GM-CPHD. IEEE Transactions on Aerospace and Electronic Systems, 2011, 47(2): 832-847.

[5] Yang K, Fu Z Q, Wang J T. Multi-sensor probability hypothesis density algorithm in multi-target filtering. Journal of Electronics&Information Tchnology, 2012, 34(6): 1368-1375.(in Chinese) 杨可, 傅忠谦, 王剑亭. 多目标滤波中的多传感器概率假设密度算法. 电子与信息学报, 2012, 34(6): 1368-1375.

[6] Mahler R. Multitarget bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178.

[7] Nagappa S, Clark D E. On the ordering of the sensors in the iterated-corrector probability hypothesis density (PHD) filter. Proceedings of SPIE, 2011: 80500M-1-6.

[8] Mahler R. Approximate multisenor CPHD and PHD filter. Proceedings of 13th Conference on Information Fusion, 2010:1-8.

[9] Vo B N, Singh S, Doucet A. Sequential monte carlo methods for multi-target filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224-1244.

[10] Delande E, Duflos E, Vanheeghe P. Multi-sensor PHD by space partitioning: computation of a true reference density within the PHD framework. IEEE Statistical Signal Processing Workshop, 2011: 333-336.

[11] Vo B T, Vo B N, Cantoni A. Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Transactions on Signal Processing, 2007, 55(7): 3553-3567.

[12] Dominicm S, Vo B T, Vo B N. A consistent metric for performance evaluation of multi-object filters. IEEE Transactions on Signal Processing, 2008, 56 (8): 3447-3457.

E-mail：hkxb@buaa.edu.cn

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异步多传感器多目标PHD航迹合成算法

PHD for Composite Tracking Algorithm Based on Asynchronous Multi-sensor Multi-target Measurements

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