Abstract:

Aiming at the problem of tracking dense target groups, especially multi-targets with micro-motion characteristics, this study proposes a dense target tracking algorithm based on label hierarchical gradual updating with micro-motion characteristics. In narrowband radar detection of dense micro-motion targets, multi-targets enter into the same resolution unit or cross-overlap, making it difficult to distinguish targets. Consequently, misdetection and omission occur in the detection and tracking processes, such as trajectory intersection and trajectory ablation, affecting detection and tracking performance. Because the micro-motion characteristics of a target have high research value in the group target detection and separation process, this study utilizes the Sinusoidal Frequency Modulated Fourier-Bessel Transform (SFMFBT) to separate the micro-motion target; simultaneously extracts the micro-motion parameters of the target, identifies the number, and extracts the target state parameters in the distance-Doppler-angle domain through signal processing; and builds the filtering updating parameter set jointly. To improve the performance of the tracking algorithm, based on the multi-target tracking filter updating mechanism of the DeepSORT framework, a labeled hierarchical gradual updating algorithm is designed for the dense target tracking trajectory intersection and other problems. A nonlinear estimation of the target state is carried out by the Unscented Kalman Filtering (UKF) to construct a multi-UKF tracker for fast and stable tracking of isotropic dense multi-targets, and the tracking parameters are extracted and numbered by signal processing. Simulation results show that the proposed algorithm can improve tracking accuracy and stability in dense target tracking tasks and has potential engineering applications.

Key words: micro-motion characteristics, DeepSORT framework, hierarchical and gradual update of label, Unscented Kalman Filtering (UKF), dense target tracking

摘要：

针对密集群目标跟踪问题, 尤其是具有微动特性的目标, 在窄带雷达探测密集微动目标的过程中, 会出现多个目标进入同一分辨单元内或者交叉重叠的问题, 难以有效地区分目标, 导致检测过程出现误检漏检和跟踪过程出现航迹交叉、航迹消融, 影响检测跟踪的性能。为此, 提出一种基于微动特性的标签分层逐级更新的密集目标跟踪算法。在群目标检测分离过程中, 目标的微动特性具有较高的研究应用价值, 因此, 利用正弦调频傅里叶-贝塞尔变换(SFMFBT)分离微动目标, 提取目标的微动参数并给出编号, 同时通过信号处理在距离-多普勒-角度域提取目标状态参数, 联合构建滤波更新参数集。为了进一步提高跟踪算法的性能, 基于DeepSORT框架的多目标跟踪滤波更新机制, 针对密集目标跟踪航迹交叉问题, 设计一种标签分层逐级更新算法, 通过无迹卡尔曼滤波(UKF)对目标状态进行非线性估计, 以构建多UKF跟踪器对同向密集多目标进行快速且稳定的跟踪。仿真实验结果表明, 所提算法在处理密集目标跟踪任务中, 能够提高跟踪的精度和稳定性, 具有良好的工程应用潜力。

关键词: 微动特性, DeepSORT框架, 标签分层逐级更新, 无迹卡尔曼滤波, 密集目标跟踪