School of Electrical Engineering, Computing, and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2019 Nov 18;19(22):5025. doi: 10.3390/s19225025.
In multitarget tracking, knowledge of the backgrounds plays a crucial role in the accuracy of the tracker. Clutter and detection probability are the two essential background parameters which are usually assumed to be known constants although they are, in fact, unknown and time varying. Incorrect values of these parameters lead to a degraded or biased performance of the tracking algorithms. This paper proposes a method for online tracking multiple targets using multiple sensors which jointly adapts to the unknown clutter rate and the probability of detection. An effective filter is developed from parallel estimation of these parameters and then feeding them into the state-of-the-art generalized labeled multi-Bernoulli filter. Provided that the fluctuation of these unknown backgrounds is slowly-varying in comparison to the rate of measurement-update data, the validity of the proposed method is demonstrated via numerical study using multistatic Doppler data.
在多目标跟踪中，对背景的了解对跟踪器的准确性起着至关重要的作用。杂波和检测概率是两个基本的背景参数，尽管它们实际上是未知的且随时间变化的，但通常被假定为已知常数。这些参数的错误值会导致跟踪算法的性能下降或出现偏差。本文提出了一种使用多个传感器在线跟踪多个目标的方法，该方法可以自适应地适应未知的杂波率和检测概率。通过对这些参数进行并行估计，并将其馈送到最先进的广义标记多伯努利滤波器中，开发了一种有效的滤波器。假设这些未知背景的波动与测量更新数据的速率相比变化缓慢，则通过使用多基地多普勒数据进行的数值研究证明了所提出方法的有效性。
