部分监测区域重叠条件下的扩展目标跟踪

J4 ›› 2016, Vol. 38 ›› Issue (06): 1231-1237.

部分监测区域重叠条件下的扩展目标跟踪

陈金广，江梦茜，马丽丽

（西安工程大学计算机科学学院,陕西西安 710048）

收稿日期:2015-05-25 修回日期:2015-07-10 出版日期:2016-06-25 发布日期:2016-06-25
基金资助:
国家自然科学基金（61201118）；中国博士后科学基金（2103M532020）；陕西省自然科学基础研究计划(2016JM6030)；西安工程大学学科建设经费资助项目

Extended target tracking under the condition of
partial overlapped multisensor monitoring regions

CHEN Jinguang，JIANG Mengxi，MA Lili

（School of Computer Science,Xi’an Polytechnic University,Xi’an 710048,China）

Received:2015-05-25 Revised:2015-07-10 Online:2016-06-25 Published:2016-06-25

摘要/Abstract

摘要：

若多传感器监测区域部分重叠，则使用传统的序贯滤波算法对扩展目标进行跟踪，会出现目标漏估计的现象。为了解决该问题，首先，在量测更新阶段使用预测步骤产生的高斯分量，而非任一传感器量测更新后的高斯分量，从而使得各个传感器滤波更新后的结果相互独立。然后，当对各传感器接收到的量测更新后，若高斯项中表示目标位置的分量落在重叠区域，则对这部分高斯项的权值进行调整。最后，对所有高斯项进行修剪与合并。仿真结果表明改进算法的有效性与精确性。

关键词: 扩展目标跟踪, 概率假设密度滤波, 多传感器数据融合, 状态估计

Abstract:

Using the traditional sequential filtering algorithm to track extended targets can lead to an underestimated number of the targets if the multisensor monitoring regions are overlapped. In order to solve this problem, firstly, the measurements are updated by using the current predicted Gaussian components rather than the updated Gaussian components which are obtained through the measurements of any sensor, so that the updated filtering results are independent among sensors. Then the measurements of all sensors are updated. The weights of the Gaussian components whose target positions locate in the overlapped regions are adjusted. Finally, all Gaussian components are pruned and merged. Simulation results show that the improved algorithm is effective and accurate.

Key words: extended target tracking;probability hypothesis density filter;multiple sensors information fusion;state estimation

陈金广，江梦茜，马丽丽. 部分监测区域重叠条件下的扩展目标跟踪[J]. J4, 2016, 38(06): 1231-1237.

CHEN Jinguang，JIANG Mengxi，MA Lili. Extended target tracking under the condition of
partial overlapped multisensor monitoring regions [J]. J4, 2016, 38(06): 1231-1237.

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