摘要
传统的最邻近联合概率数据关联算法(NNJPDA)不能直接用于多传感器对多目标的跟踪。针对这一问题,提出了一种适用于多传感器多目标跟踪的最邻近联合概率数据关联算法,它以极大似然估计完成对来自多传感器的测量集合进行同源最优划分,然后采用NNJPDA方法对多目标进行跟踪。经过理论分析和仿真试验,证明了该方法能有效地进行多传感器多目标的跟踪,且具有算法简单、跟踪精度高、附加计算量小等优点。
The Nearest Near Joint Probabilistic Data Association （NNJPDA） is not used directly in multi - sensor multi - target tracking. This paper presents a method of implementing multi - sensor multi - target tracking by combining maximum likelihood estimation with the Nearest Near Joint Probabilistic Data Association （NNJPDA）. The maximum likelihood estimation is used to classify the same source observations at one time into the same set, and then NNJPDA is used to implement multi -target tracking. The theoretical analysis and computer simulation show that this algorithm can achieve multi -sensor multi -target tracking perfectly with low calculation load added and higher precision.
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2006年第4期39-42,共4页
Journal of Air Force Engineering University(Natural Science Edition)
基金
军队科研基金资助项目
关键词
多传感器多目标跟踪
极大似然估计
最邻近联合概率数据关联
位置融合
multi -sensor multi -target tracking
maximum likelihood estimation
Nearest Near Joint Probabilistic Data Association
position fusion
作者简介
侯蒙（1982-），男，河北秦皇岛人，硕士生，主要从事信号与信息处理研究．
