基于区实混合序列相似度的异步不等速率航迹关联算法

在分布式多目标跟踪系统中,由于局部传感器开机时间、采样频率以及通信延迟不同等原因,导致来自各传感器的局部航迹往往是异步不等速率的。目前一般的方法是先进行时域配准再进行航迹关联,但是在同步化的过程中,航迹估计值的误差会发生传播,影响航迹关联的性能。针对此问题,提出了一种基于区实混合序列相似度的异步不等速率航迹关联算法。算法首先通过区间数-实数混合序列变换(IRST)得到等长度的航迹行为序列,然后定义一种新的序列差异信息度量,得到混合序列的相似度,以此进行航迹关联判定。仿真实验表明,该算法可以有效地解决异步不等速率航迹关联问题,并且通信延迟和数据乱序对算法性能的影响不明显。

Asynchronous track-to-track association algorithm based on similarity degree of interval-real sequence

Because local sensors in the distributed multi-target tracking system usually start working at different time and provide tracks at different rates with different communication delays, the local tracks from different sensors are usually asynchronous. The current solution is to synchronize the tracks before track association. But the estimation error spreads when synchronizing, which affects the performance of correlation. To solve the problem, an asynchronous track-to-track association method based on similarity degree of interval-real sequence is presented. Firstly, the track sequences are transformed to same-length sequences which contain interval data and real data by interval-real sequence transform (IRST). Then a new difference measurement for the sequences is defined, by which the correlation degree can be calculated and the track association conclusion be made. Simulation results show that the presented method can effectively solve the asynchronous track-to-track association problem, and its performance is seldom affected in the case of different communication delays and disorderly data.