锥扫型光学传感器像平面多目标轨迹跟踪

TP391; 积分概率多假设跟踪(IPMHT)是一种基于期望极大化(EM)的准最优贝叶斯多目标迭代跟踪算法,研究了该算法在锥扫型光学传感器像平面多目标轨迹跟踪中的问题.为提高算法的跟踪性能和计算效率,利用逻辑概率数据关联滤波(PDAF)方法进行目标初始状态估计,并利用目标幅度信息和波门技术对IPMHT进行优化.针对锥扫型传感器非线性观测下的多目标跟踪,将扩展无味卡尔曼滤波(AUKF)与优化的IPMHT算法相结合,实现像平面多目标轨迹的起始、维持和终结.蒙特卡洛仿真实验表明,该算法成功地解决了锥扫型传感器的像平面多目标轨迹跟踪问题,在提高目标跟踪性能的同时改善了计算效率....

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Published inTongxin Xuebao Vol. 32; no. 9; pp. 123 - 128
Main Authors 龙云利, 盛卫东, 徐晖, 安玮
Format Journal Article
LanguageChinese
Published 国防科学技术大学电子科学与工程学院,湖南长沙410073 2011
75122部队,广西桂林541007%国防科学技术大学电子科学与工程学院,湖南长沙,410073
Editorial Department of Journal on Communications
Subjects
期望极大化
扩展无味卡尔曼滤波
多目标跟踪
积分概率多假设跟踪
Online AccessGet full text
ISSN1000-436X
DOI10.3969/j.issn.1000-436X.2011.09.018

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Abstract TP391; 积分概率多假设跟踪(IPMHT)是一种基于期望极大化(EM)的准最优贝叶斯多目标迭代跟踪算法,研究了该算法在锥扫型光学传感器像平面多目标轨迹跟踪中的问题.为提高算法的跟踪性能和计算效率,利用逻辑概率数据关联滤波(PDAF)方法进行目标初始状态估计,并利用目标幅度信息和波门技术对IPMHT进行优化.针对锥扫型传感器非线性观测下的多目标跟踪,将扩展无味卡尔曼滤波(AUKF)与优化的IPMHT算法相结合,实现像平面多目标轨迹的起始、维持和终结.蒙特卡洛仿真实验表明,该算法成功地解决了锥扫型传感器的像平面多目标轨迹跟踪问题,在提高目标跟踪性能的同时改善了计算效率.
AbstractList TP391; 积分概率多假设跟踪(IPMHT)是一种基于期望极大化(EM)的准最优贝叶斯多目标迭代跟踪算法,研究了该算法在锥扫型光学传感器像平面多目标轨迹跟踪中的问题.为提高算法的跟踪性能和计算效率,利用逻辑概率数据关联滤波(PDAF)方法进行目标初始状态估计,并利用目标幅度信息和波门技术对IPMHT进行优化.针对锥扫型传感器非线性观测下的多目标跟踪,将扩展无味卡尔曼滤波(AUKF)与优化的IPMHT算法相结合,实现像平面多目标轨迹的起始、维持和终结.蒙特卡洛仿真实验表明,该算法成功地解决了锥扫型传感器的像平面多目标轨迹跟踪问题,在提高目标跟踪性能的同时改善了计算效率.
Author 龙云利
盛卫东
徐晖
安玮
AuthorAffiliation 国防科学技术大学电子科学与工程学院,湖南长沙410073;75122部队,广西桂林541007%国防科学技术大学电子科学与工程学院,湖南长沙,410073
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SHENG Wei-dong
XU Hui
AN Wei
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Keywords 期望极大化
扩展无味卡尔曼滤波
多目标跟踪
积分概率多假设跟踪
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