School of Electronics & Information Engineering, Beihang University, Beijing 100191, China.
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.
Sensors (Basel). 2020 Aug 26;20(17):4816. doi: 10.3390/s20174816.
In multi-target tracking scenarios with dense and heterogeneous clutter, there is a substantial increase in the false measurements that originated from the clutter within the validation gate, and consequently, the number of measurement-to-track association hypothesis grows rapidly in traditional multiple hypothesis tracker (MHT), leading to a sharp decrease in data association accuracy and tracking performance. A new multiple hypothesis tracker using validation gate with motion direction constraint (MHT-MDC) is proposed to solve these problems. In the MHT-MDC, a motion direction constraint (MDC) gate is designed by considering the prior target maneuvering information, which effectively reduces the volume of validation gate and, thus, diminishes the number of false measurements in the gate when the innovation covariance is large. Subsequently, the clutter density in the MDC gate is adaptively estimated by the conditional mean estimator of clutter density (CMECD), based on which the score functions in the MDC gate can be calculated. The MHT-MDC is compared with the MHT algorithm in simulations, and the experimental results demonstrate its superior tracking performance for weakly maneuvering targets in high clutter density scenarios.
在具有密集和异质杂波的多目标跟踪场景中，验证门内的杂波会产生大量的虚假测量值，因此，传统的多假设跟踪器（MHT）中的测量值与目标关联假设的数量会迅速增加，从而导致数据关联准确性和跟踪性能急剧下降。为了解决这些问题，提出了一种新的使用具有运动方向约束的验证门的多假设跟踪器（MHT-MDC）。在 MHT-MDC 中，通过考虑先验目标机动信息设计运动方向约束（MDC）门，当新息协方差较大时，有效地减小了验证门的体积，从而减少了门内的虚假测量值数量。随后，基于杂波密度条件均值估计器（CMECD）自适应估计 MDC 门内的杂波密度，根据该密度计算 MDC 门内的评分函数。通过仿真将 MHT-MDC 与 MHT 算法进行比较，实验结果表明，该算法在高杂波密度场景下对弱机动目标具有优越的跟踪性能。
