似然值波门SMC-PHD滤波器

doi:10.3969/j.issn.1006-2475.2017.11.020

计算机与现代化

似然值波门SMC-PHD滤波器

(1.河海大学能源与电气学院，江苏南京 211100; 2.河海大学计算机与信息学院阵列与信息处理实验室，江苏南京 211100)

收稿日期:2017-08-14 出版日期:2017-11-21 发布日期:2017-11-21
作者简介:高乙月(1983-)，女，江苏泗洪人，河海大学能源与电气学院实验师，河海大学计算机与信息学院阵列与信息处理实验室博士研究生，研究方向：多目标跟踪，目标识别，雷达数字信号处理; 蒋德富(1963-)，男，教授，博士生导师，硕士，研究方向：阵列天线及阵列信号处理，雷达通信集成系统的跟踪制导及目标识别; 刘铭(1989-)，男，博士研究生，研究方向：雷达数字信号处理，目标跟踪，目标识别; 付伟(1990-)，男，博士研究生，研究方向：阵列信号处理，数字波束合成。
基金资助:
江苏高校优势学科建设工程资助项目; 国防科学技术预先研究基金资助项目(404405040301)

Likelihood-gating Sequential Monte Carlo Probability Hypothesis Density Filter

(1. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China; 2. Array and Information Processing Laboratory, College of Computer and Information, Hohai University, Nanjing 211100, China)

Received:2017-08-14 Online:2017-11-21 Published:2017-11-21

摘要/Abstract

摘要： 概率假设密度(Probability Hypothesis Density, PHD)滤波器的序贯蒙特卡罗(Sequential Monte Carlo, SMC)实现需要大量的粒子。为了解决其计算的有效性，本文提出一种改进的SMC-PHD滤波器，称之为似然值波门SMC-PHD滤波器。首先，以所有预测粒子为依据，利用全部的多目标后验信息，最大限度地确认出所有目标生成的观测。其次，基于校正器中所有预测粒子的似然值，避免为粒子贴标签以及传统的距离计算，使得算法在各种应用中易于实现，只有有效观测才参与粒子权值的更新。最后，与基本SMC-PHD滤波器相比，其优秀的实时性和更好的滤波精度通过仿真得到证实。

关键词: 多目标跟踪, 概率假设密度滤波, 序贯蒙特卡罗, 波门, 剔除杂波

Abstract: To resolve the low computational efficiency of the sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter, which requires a large number of particles, we propose an improved SMC-PHD filter called the likelihood-gating SMC-PHD filter. Firstly, based on all the predicted particles, the maximum number of actual surviving observations can be selected, as all multi-target posterior information is utilized. Secondly, based on all the likelihood values of predicted particles in the updater, the proposed filter can be easily implemented in various applications, as it obviates labeling the particles and calculating the distances. Only the effective observations are employed to update the weight of particles. Experiments show that this filter has excellent real-time performance and better filtering accuracy compared with the basic SMC-PHD filter.

Key words: multi-target tracking, probability hypothesis density filter, sequential Monte Carlo, gating, rejecting clutter

中图分类号:

TP391

高乙月1,2，蒋德富2，刘铭2，付伟2. 似然值波门SMC-PHD滤波器[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2017.11.020.

GAO Yi-yue1,2, JIANG De-fu2, LIU Ming2, FU Wei2. Likelihood-gating Sequential Monte Carlo Probability Hypothesis Density Filter[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.11.020.

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似然值波门SMC-PHD滤波器

Likelihood-gating Sequential Monte Carlo Probability Hypothesis Density Filter

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