School of Electrical Engineering, Computing, and Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia 2 School of Computer Science, The University of Adelaide, Adelaide, SA 5005, Australia.
Current address: ThaiNguyen University of Technology, ThaiNguyen University, ThaiNguyen 251810, Vietnam..
Sensors (Basel). 2019 Apr 8;19(7):1672. doi: 10.3390/s19071672.
The measurements from multistatic radar systems are typically subjected to complicated data association, noise corruption, missed detection, and false alarms. Moreover, most of the current multistatic Doppler radar-based approaches in multitarget tracking are based on the assumption of known detection probability. This assumption can lead to biased or even complete corruption of estimation results. This paper proposes a method for tracking multiple targets from multistatic Doppler radar with unknown detection probability. A closed form labeled multitarget Bayes filter was used to track unknown and time-varying targets with unknown probability of detection in the presence of clutter, misdetection, and association uncertainty. The efficiency of the proposed algorithm was illustrated via numerical simulation examples.
多基地雷达系统的测量结果通常需要进行复杂的数据关联、噪声干扰、漏检和虚警处理。此外，目前大多数基于多基地多普勒雷达的多目标跟踪方法都基于检测概率已知的假设。这种假设可能会导致估计结果出现偏差，甚至完全失效。本文提出了一种用于跟踪多基地多普勒雷达中具有未知检测概率的多个目标的方法。使用闭式标记多目标贝叶斯滤波器来跟踪在杂波、漏检和关联不确定性存在的情况下具有未知检测概率的未知和时变目标。通过数值仿真示例说明了所提出算法的有效性。
