| 基于最优划分的多传感器多目标跟踪NNJPDA算法 |
| |
| 引用本文： | 侯蒙,王睿. 基于最优划分的多传感器多目标跟踪NNJPDA算法[J]. 空军工程大学学报(自然科学版), 2006, 7(4): 39-42 |
| |
| 作者姓名： | 侯蒙 王睿 |
| |
| 作者单位： | 空军工程大学,导弹学院,陕西,三原,713800;空军工程大学,导弹学院,陕西,三原,713800 |
| |
| 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。
|
| 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 |
| 文章编号： | 1009-3516（2006）04-0039-04 |
| 收稿时间： | 2005-10-20 |
| 修稿时间： | 2005-10-20 |
NNJPDA in Multi-sensor Multi-target Tracking Based on Optimization Partition |
| |
| HOU Meng,WANG Rui. NNJPDA in Multi-sensor Multi-target Tracking Based on Optimization Partition[J]. Journal of Air Force Engineering University(Natural Science Edition), 2006, 7(4): 39-42 |
| |
| Authors: | HOU Meng WANG Rui |
| |
| Affiliation: | The Missile Institute, Air Force Engineering University, Sanyuan, Shaanxi 713800, China |
| |
| Abstract: | 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. |
| |
| Keywords: | multi-sensor multi-target tracking maximum likelihood estimation Nearest Near Joint Probabilistic Data Association position fusion |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《空军工程大学学报(自然科学版)》浏览原始摘要信息 |
|
点击此处可从《空军工程大学学报(自然科学版)》下载免费的PDF全文 |
