基于航迹相似度的分布式系统目标跟踪算法

Distributed System Target Tracking Algorithm Based On Track Comparability Degree

  • 摘要: 航迹关联与航迹融合是分布式目标跟踪系统跟踪目标的关键,本文提出了基于航迹相似度的目标动态加权融合跟踪算法。综合各传感器航迹估计形成的目标航迹特征向量与传感器分辨率性能,根据模糊聚类算法建立各观测时刻航迹隶属度相似性度量矩阵与系统航迹关联决策矩阵,解决融合中心航迹关联问题。根据加权融合算法思想,结合各观测时刻航迹隶属度相似性度量矩阵,实时、动态分配融合中心航迹号集合中各局部航迹权值,解决目标航迹融合问题。蒙特卡罗仿真表明,算法在融合中心能正确进行航迹关联与航迹融合,实现对观测区域内所有目标的有效跟踪,并且得到比单个传感器局部航迹跟踪误差更小的目标系统融合航迹,为分布式目标跟踪系统提供了一种可靠有效的目标跟踪方法。

     

    Abstract: Track association and track fusion are the key problems of tracking target in distributed target tracking system,a dynamic weighted fusion tracking algorithm based on track comparability degree is proposed in this paper.By integrating the track character vector provided by track estimation of each local sensor and the performance of sensor resolution,track fuzzy membership comparability degree matrix and systemic track association decision matrix at every measurement time are established on the basis of Fuzzy Clustering Method,and the problem of track association in fusion center is solved.Combining with the idea of weighted fusion algorithm and track fuzzy membership comparability degree matrix at every measurement time,real time and dynamic weighting factors of all local tracks in fusion center track aggregation are alloted,then the problem of track fusion in fusion center is solved.Monte Carlo simulation shows that with the proposed algorithm track association and track fusion in fusion center can be correctly processed,thus the system can track all targets in the observation area effectively,and obtain the systemic fusion tracks of targets that have smaller tracking errors than individual local sensor tracks,simulation results also show that the algorithm proposed in this paper is an effective tracking method for distributed target tracking system.

     

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