基于稀疏度自适应匹配追踪算法的微动杂波抑制

Micro-Doppler clutter suppression based on sparsity adaptive matching pursuit algorithm

摘要:

目标或目标组成部分的机械振动或旋转产生微多普勒效应，在目标分类和识别中起着重要作用。然而，环境中许多物体(例如风力涡轮机、空调等)的微多普勒效应对雷达系统而言就像时变的杂波，导致雷达探测性能下降。本文针对外辐射源雷达微动杂波影响目标检测的问题，提出了一种基于稀疏度自适应匹配追踪改进算法(SAMP)的微动杂波抑制方法。考虑到微动杂波的稀疏特性，将复杂的微动杂波抑制问题转化为稀疏信号表示问题，分离微动杂波并将其抑制，便于目标观测。相比于原SAMP算法，改进后的SAMP算法能自动调整步长并在残差达到自适应阈值后快速停止迭代。仿真和实测数据验证了所提方法的有效性。

Abstract:

The mechanical vibration or rotation of the target or its components produces the micro-Doppler effect, which plays an important role in target classification and recognition. However, the micro-Doppler effect of many objects in the environment (such as wind turbines, air conditioners, etc.) is like time-varying clutter to the radar system, leading to false alarms. In this paper, aiming at the problem that micro-Doppler clutter may interfere with passive radar target detection, a micro-Doppler clutter suppression method based on the improved Sparsity of Adaptive Matching Pursuit (SAMP) is proposed. Considering the sparse characteristics of the micro-Doppler clutter, this problem can be transformed into a sparse signal representation problem, and the purpose of suppressing the micro-Doppler clutter is achieved by separating the signal. Compared with the original SAMP algorithm, the improved SAMP algorithm can quickly stop iteration after the residual reaches the adaptive threshold. Simulation and measured data verify the effectiveness of the proposed method.