基于PCA-CLEAN的噪声稳健激光微多普勒特征提取方法

Noise robust laser micro-Doppler feature extraction method based on PCA-CLEAN

雷达回波中的微多普勒效应能够反映目标的几何结构和运动特性,作为目标独一无二的特征,能够用来实现对目标类别和属性的判断。波长的优势使激光雷达相对于微波雷达具备更好的微多普勒探测精度。针对空中飞机目标(直升机、螺旋桨飞机)回波中微多普勒调制能量较弱,易被噪声污染的问题,提一种基于PCA-CLEAN的噪声稳健激光微多普勒特征提取方法,首先利用PCA对回波信号进行噪声抑制,然后利用CLEAN算法将回波中的机身分量和微动分量区分开,进而提取反映不同目标微动差异的三维特征进行目标分类,基于仿真和实测数据的实验结果表明,所提方法能够获得较好的分类性能,同时在低信噪比条件下能够获得较好的噪声抑制性能。

The micro-Doppler effect in radar echo reflects the geometric structure and motion characteristics of the target,which is the unique feature of the target,can be used to judge the target category and attribute.The advantage of wavelength makes liars have better detection accuracy than microwave radar.In order to solve the problem that the micro Doppler modulation energy in the airborne target echo is weak and easy to be polluted by noise,a robust laser micro Doppler feature extraction method based on PCA-CLEAN is proposed.Firstly,PCA is used to suppress the noise of the echo signal,and then clean algorithm is used to distinguish the fuselage component and the micro motion component in the echo,and then three micro motion differences reflecting different targets are extracted.The experimental results show that the proposed method can achieve better classification performance and better noise suppression performance under low SNR.