基于复数局部均值分解的含旋转部件目标微多普勒分离技术

Micro-Doppler Separation from Targets with Rotating Parts Based on Complex Local Mean Decomposition

针对逆合成孔径雷达成像中某些含旋转部件的雷达目标,其回波由于受到旋转部件微多普勒的影响,从而导致目标主体成像质量下降的问题,该文研究了基于复数局部均值分解的微多普勒分离方法。该文通过分析目标主体和旋转部件回波分量的多普勒差异,并利用复数局部均值分解方法精确分离信号中内含的振荡模式,自适应地从高频至低频将复杂非平稳信号分解成若干个平稳的单分量信号,从而实现微多普勒信号分离。通过将微多普勒信号和目标主体回波进行分离,可以提高目标主体的成像质量,并能更好地获得旋转部件的微动参数特征。仿真和实测数据的结果证明了该方法的有效性。

In the ISAR imaging of targets with rotating parts, the image of the targets＇ main body may be shadowed by the micro-Doppler effect induced by the rotating parts. To solve this problem, an algorithm utilizing complex local mean decomposition is researched to separate the micro-Doppler signature. The Doppler frequency difference between the main body and the rotating parts is analyzed, and the complex local mean decomposition method is utilized to separate the micro-Doppler signature which accurately separate the oscillation mode embedded in signals and decompose the complex modulation non-stationary signals adaptively into some stable mono-components. After the separation, better geometrical features of the main body and micro-Doppler features of the rotating parts can be obtained by processing each independently. The results from the simulated and measured data are given to verify the validity of the proposed algorithm.