基于变分模态分解和压缩感知的弱观测条件下雷达信号重构方法

针对弱观测条件下雷达信号存在数据残损的问题，该文提出一种基于变分模态分解和压缩感知(VMD-CS)的雷达信号重构方法。首先通过变分模态分解对采样数据进行降解去噪处理，其次在压缩感知框架下构造观测矩阵、稀疏表示字典矩阵，然后基于正交追踪匹配(OMP)算法重构出稀疏表示向量。在此基础上利用离散余弦稀疏矩阵重构信号，实现对残损雷达信号的数据重构。在连续丢失数据和随机丢失数据两种情况下，对实际采集的线性调频(LFM)雷达信号进行仿真实验。实验结果表明:在数据连续丢失率不高于30%或随机丢失率不高于60%的情况下，该文方法能有效重构雷达信号，在时域、频域和瞬时频率上能够准确逼近原始信号。

Damaged Radar Signal Reconstruction Method Based on Variational Mode Decomposition and Compressed Sensing

In view of the problem of data damage in faint radar signals, a radar signal reconstruction method is proposed based on Variational Mode Decomposition and Compressed Sensing (VMD-CS). Firstly, Variational Mode Decomposition is used to degrade and denoise the collected data. Secondly, the observation matrix and sparse representation matrix are constructed by compressed sensing method. Then the sparse representation vector is reconstructed based on the Orthogonal Matching Pursuit (OMP) algorithm. On this basis, the discrete cosine transform is utilized to reconstruct the damaged radar signal. The simulation experiments are carried out on the actual collect Linear Frequency Modulation (LFM) radar signal in two cases of continuous data loss and random data loss. The experimental results show that, the proposed method can well reconstruct the radar signal and approach the original signal accurately in time domain, frequency domain and instantaneous frequency when the continuous data loss rate does not exceed 30% or the random data loss date does not exceed 60%.