He Shaoming, Shin Hyo-Sang, Tsourdos Antonios
School of Aerospace, Transport and Manufacturing, Cranfield University, MK43 0AL Cranfield, UK.
Sensors (Basel). 2018 Jan 18;18(1):269. doi: 10.3390/s18010269.
This paper proposes a novel joint probabilistic data association (JPDA) filter for joint target tracking and track maintenance under unknown detection probability and clutter rate. The proposed algorithm consists of two main parts: (1) the standard JPDA filter with a Poisson point process birth model for multi-object state estimation; and (2) a multi-Bernoulli filter for detection probability and clutter rate estimation. The performance of the proposed JPDA filter is evaluated through empirical tests. The results of the empirical tests show that the proposed JPDA filter has comparable performance with ideal JPDA that is assumed to have perfect knowledge of detection probability and clutter rate. Therefore, the algorithm developed is practical and could be implemented in a wide range of applications.
本文提出了一种新颖的联合概率数据关联（JPDA）滤波器，用于在未知检测概率和杂波率的情况下进行联合目标跟踪与航迹维护。所提出的算法主要由两部分组成：（1）具有用于多目标状态估计的泊松点过程出生模型的标准JPDA滤波器；（2）用于检测概率和杂波率估计的多伯努利滤波器。通过实证测试对所提出的JPDA滤波器的性能进行了评估。实证测试结果表明，所提出的JPDA滤波器与假定具有完美检测概率和杂波率知识的理想JPDA具有相当的性能。因此，所开发的算法是实用的，可在广泛的应用中实现。
