粒子滤波理论、方法及其在多目标跟踪中的应用

李天成; 范红旗; 孙树栋

doi:10.16383/j.aas.2015.c150426

粒子滤波理论、方法及其在多目标跟踪中的应用

doi: 10.16383/j.aas.2015.c150426

1.
西北工业大学机电学院西安 710072 中国;
2.
Salamanca大学科学学院 Salamanca 37008 西班牙;
3.
国防科技大学ATR重点实验室长沙 410073 中国

基金项目:

国家自然科学基金(51475383),国家自然科学基金青年基金(61101186),航空科学基金(20110112006)资助

详细信息

作者简介:
范红旗国防科学技术大学自动目标识别重点实验室副教授. 2008 年获得国防科学技术大学信息与通信工程博士学位.主要研究方向为雷达信号与数据处理,目标跟踪与信息融合, 多智能体系统.E-mail: fhongqi@gmail.com

通讯作者:
李天成西班牙萨拉曼卡大学科学院BISITE 组博士后, 2008 年获得哈尔滨工程大学工学与管理学(辅) 双学士学位, 2013 年获得英国伦敦南岸大学电子电气工程专业博士学位, 2015 年获得西北工业大学机械电子工程专业博士学位.主要研究方向为统计信号处理、信息融合与分布式计算, 特别是粒子滤波以及多目标跟踪.本文通信作者.

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出版历程
- 收稿日期: 2015-07-06
- 修回日期: 2015-11-02
- 刊出日期: 2015-12-20

Particle Filtering: Theory, Approach, and Application for Multitarget Tracking

1.
School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
2.
School of Science, University of Salamanca, Salamanca 37008, Spain;
3.
ATR Laboratory, National University of Defense Technology, Changsha 410073, China

Funds:

Supported by National Natural Science Foundation of China (51475383), National Natural Science Foundation for Distinguished Young Scholar (61101186), Aviation Science Foundation (20110112006)

摘要

摘要: 本文梳理了粒子滤波理论基本内容、发展脉络和最新研究进展, 特别是对其在多目标跟踪应用中的一系列难点问题与主流解决思路进行了详细分析和报道. 常规粒子滤波研究重点主要围绕重要性采样函数、计算效率、权值退化/样本匮乏和复杂系统建模展开. 作为一类复杂估计问题,多目标跟踪一方面需要准确的目标新生/消亡与演变、虚警/漏检等建模技术, 另一方面需要多传感器信息融合、航迹管理等复杂决策方法.暨有限集统计学应用于多目标跟踪后,粒子滤波进入一个新的发展阶段---随机集粒子滤波.基于不同的背景假设,可以构建不同近似形式的随机集贝叶斯滤波器并采用粒子滤波实现.但机动目标、未知场景、多目标航迹管理以及跟踪性能评价等仍是多目标粒子滤波的研究难点和重点.
- 贝叶斯估计 /
- 非线性滤波 /
- 粒子滤波 /
- 有限集统计学 /
- 多目标跟踪
Abstract: This paper reviews the theory and state-of-the-art developments of the particle filter with emphasis on the remaining challenges and corresponding solutions in the context of multitarget tracking. The research focuses of the general particle filter lie on importance proposal, computing efficiency, weight degeneracy, sample impoverishment, and complicated system modelling. Multi-target tracking involves a class of complex dynamic estimation problems that require both accurate models for target birth, death and evolution, false alarms and miss-detections, and efficient decision-making strategies regarding multi-sensor data fusion and track management. Specifically, with the introduction of finite set statistics to multi-target tracking, recent years have seen the burgeoning development of a new generation of particle filters, which is referred to as the random set particle filter in this paper. Based on different scenario assumptions, different approximate forms of random set Bayesian filters can be established and implemented by the particle filter. However, manoeuvring target, unknown scenario, track management and tracker performance assessment remain key challenges for the multi-target tracking particle filter.
- Bayesian estimation /
- nonlinear filtering /
- particle filter /
- finite set statistics /
- multi-target tracking

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