两级分辨率稀疏重构线性调频信号参数估计

Two Level Resolution Compressed Sensing Parameter Estimation for LFM Signal

为解决压缩感知(Compressed Sensing,CS)算法在线性调频(Linear Frequency Modulation,LFM)信号参数估计中计算量较大的问题,提出了两级分辨率稀疏重构参数估计算法。该算法在离散线性调频傅里叶变换的基础上,采用低分辨率观测矩阵,获取信号调频率和中心频率的先验信息,根据先验信息构造有约束的高分辨率观测矩阵,精确估计出调频率和中心频率两个参数,实现LFM信号的重构,达到了减小计算量的目的。仿真实验表明,该算法能够准确估计单个和多个LFM信号的参数,并且算法的参数估计性能明显优于传统算法。

In order to solve the problem of computational complexity of the Compressed Sensing(CS)algorithm in the parameter estimation of Linear Frequency Modulation(LFM)signal,a two-level resolution sparse reconstruction parameter estimation algorithm is proposed.On the basis of discrete linear frequency modulation Fourier transform,a low-resolution observation matrix is used to obtain the prior information of signal frequency modulation and center frequency.The prior information is used to construct a constrained high-resolution observation matrix.The parameters of frequency modulation and center frequency are accurately estimated.The reconstruction of LFM signal is realized,which achieves the purpose of reducing the computational load.The simulation results show that the parameters of single LFM signal and multiple LFM signals can be accurately estimated by the algorithm,and the performance of the algorithm is obviously better than that of the traditional algorithm.