基于箱粒子滤波的鲁棒标签多伯努利跟踪算法

doi:10.3969/j.issn.1000-1093.2017.10.024

兵工学报 ›› 2017, Vol. 38 ›› Issue (10): 2062-2068.doi: 10.3969/j.issn.1000-1093.2017.10.024

基于箱粒子滤波的鲁棒标签多伯努利跟踪算法

魏帅¹, 冯新喜¹, 王泉¹, 鹿传国²

(1.空军工程大学信息与导航学院，陕西西安 710077； 2.95806部队，北京 100076)

收稿日期:2017-03-20 修回日期:2017-03-20 上线日期:2017-11-22
通讯作者: 冯新喜（1962—），男，教授，博士生导师 E-mail:tear0419@qq.com
作者简介:魏帅(1993—)，女，硕士研究生。E-mail:swei@stu.xidian.edu.cn
基金资助:
国家自然科学基金项目(61571458)；陕西省自然科学基金项目(2011JM8023)

Robust Labeled Multi-Bernoulli Tracking Algorithm Based on Box Particle Filtering

WEI Shuai¹, FENG Xin-xi¹, WANG Quan¹, LU Chuan-guo²

(1.Information and Navigation College, Air Force Engineering University, Xi'an 710077, Shaanxi, China; 2.Unit 95806 of PLA, Beijing 100076, China）

Received:2017-03-20 Revised:2017-03-20 Online:2017-11-22

摘要/Abstract

摘要： 针对在未知杂波和检测概率的跟踪环境中标准的标签多伯努利（LMB）算法跟踪精度较低、粒子覆盖集过大致使复杂度较高的问题，引入区间分析技术，提出基于箱粒子滤波的鲁棒LMB跟踪算法。建立目标增广空间模型，基于箱粒子滤波方法，推导出有杂波状态标签和LMB元素标签的预测、更新方程，并用多目标箱粒子LMB滤波递推估计目标状态。仿真结果表明，当杂波和检测概率先验未知，与现有非标签、非鲁棒算法相比，所提算法可实现在低检测概率和高杂波强度环境下对目标的稳定跟踪，同时大幅度提高算法的运行效率。

关键词: 控制科学与技术, 多目标跟踪, 区间分析, 标签多伯努利, 箱粒子, 鲁棒滤波器

Abstract: The standard labeled Bernoulli (LMB) filter cannot guarantee a higher tracking performance, and multitude number of particles leads to the longer operation time of algorithm under the conditions of unknown clutter and detection probability. A robust labeled multi-Bernoulli algorithm based on box particle filtering is proposed. An augmented state space model is established, and the prediction and update state recursion equations with clutter state labels and LMB element labels are derived based on box particle filtering. The state of multi-target is recursively estimated using LMB filter box particles. Simulation reveals that the proposed algorithm has a better performance in target tracking under the conditions of unknown clutter and detection probability, and dramatically reduces the computation time with higher tracking accuracy under the conditions of lower detection probability and higher clutter ratet compared with the conventional algorithm with non-label and non-robustness. Key

Key words: controlscienceandtechnology, multi-targettracking, intervalanalysis, labeledmulti-Bernoulli, boxparticle, robustfilter

中图分类号:

TN953⁺.6

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.

WEI Shuai, FENG Xin-xi, WANG Quan, LU Chuan-guo. Robust Labeled Multi-Bernoulli Tracking Algorithm Based on Box Particle Filtering[J]. Acta Armamentarii, 2017, 38(10): 2062-2068.

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基于箱粒子滤波的鲁棒标签多伯努利跟踪算法

Robust Labeled Multi-Bernoulli Tracking Algorithm Based on Box Particle Filtering

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