Tao Jin, Jiang Defu, Yang Jialin, Han Yan, Wang Song, Lu Xingchen
School of Computer and Information, Hohai University, Nanjing, 210098, China.
Laboratory of Array and Information Processing, Hohai University, Nanjing, 210098, China.
Sci Rep. 2024 Mar 4;14(1):5279. doi: 10.1038/s41598-024-56065-7.
Since probability hypothesis density (PHD) filters do not need explicit data association, they have recently been widely used in radar multi-target tracking (MTT). However, in existing PHD filters, sampling times are generally considered the same for all targets. Due to the limitation of antenna beam width in radar applications, the same sampling time for all targets will lead to a mismatch between the predicted data and measurement data, reducing the accuracy of radar MTT. In order to eliminate the estimation error with less computational cost, a radar nonlinear multi-target tracking method with a parallel PHD filter is proposed in this article. The measurement area is divided into several subspaces according to the beam width of the radar antenna, and the PHD of all subspaces is calculated in parallel. Then, multi-feature information in radar echo assists tracking and improves real-time performance. Experimental results in various scenarios illustrate that the proposed method can eliminate the estimation errors introduced by sampling time diversity at the cost of less computation cost, especially in cluttered environments.
由于概率假设密度（PHD）滤波器不需要显式的数据关联，近年来它们在雷达多目标跟踪（MTT）中得到了广泛应用。然而，在现有的PHD滤波器中，通常认为所有目标的采样时间相同。由于雷达应用中天线波束宽度的限制，所有目标采用相同的采样时间会导致预测数据与测量数据不匹配，降低雷达多目标跟踪的精度。为了以较低的计算成本消除估计误差，本文提出了一种基于并行PHD滤波器的雷达非线性多目标跟踪方法。根据雷达天线的波束宽度将测量区域划分为若干子空间，并并行计算所有子空间的PHD。然后，雷达回波中的多特征信息辅助跟踪并提高实时性能。各种场景下的实验结果表明，该方法能够以较低的计算成本消除采样时间差异引入的估计误差，尤其是在杂波环境中。
