基于贝塞尔函数基信号分解的微动群目标特征提取方法

Micro-Doppler Feature Extraction of Group Targets Using Signal Decomposition Based on Bessel Function Basis

微动特征提取是群目标分辨的有效手段,以往针对孤立目标的特征提取技术不再适用。针对此该文提出了一种基于信号分解的微动群目标特征提取方法。首先通过分析微动信号的正弦调频(SFM)形式,推导了SFM信号相位项在k-分辨率贝塞尔函数基上的分解结果;然后根据回波分解结果中微动频率与函数基的一一对应关系进行频率粗略估计,并针对误差产生原因给出了精确的微动频率估计方法;最后在离散信号相位解模糊的基础上,完成各子目标的微动频率提取。仿真实验验证了算法的有效性,且与正弦调频傅里叶变换(SFMFT)算法和平均幅度差函数(AMDF)算法相比具有更高精度。

Micro-Doppler （m-D） feature extraction is significant for group target discrimination, while the methods for single target are invalid. An m-D feature extraction method of group targets is proposed based on signal orthogonal decomposition. First, the Sinusoidal Frequency-Modulated （SFM） form of m-D signals and the decomposition result of the phase term onk-resolution Bessel basis is deduced. The m-D frequency is coarsely estimated by the one-to-one relationship between frequencies and basis functions. Then an algorithm is introduced to reduce the error and thus a finer estimation is obtained. Finally, the m-D frequency of each target is extracted by discrete echoes without phase shift ambiguity. Simulation experiments validate the effectiveness, and show that the proposed method outperforms the Sinusoidal Frequency Modulation Fourier Transform （SFMFT）-based method and Average Magnitude Difference Function （AMDF）-based method in estimation precision.