欠采样条件下基于DCS的LFM信号带宽估计方法

Bandwidth estimation method of LFM signal based on DCS under unsampled conditions

摘要:

针对传统方法不适用于欠采样条件下线性调频(LFM)信号在低信噪比(SNR)条件下带宽估计问题，提出一种基于分布式压缩感知(DCS)的带宽估计方法，利用同一信源多个脉冲的联合稀疏特性进行LFM信号带宽估计。首先构建LFM欠采样信号模型，其次利用DCS算法对LFM带宽进行联合稀疏重构，然后分析了所提LFM信号带宽估计方法性能，最后利用仿真验证了方法的可行性和有效性。

Abstract:

Aiming at the problem that traditional methods cannot estimate the bandwidth of under-sampled Linear Frequency Modulation(LFM) signals under low Signal Noise Ratio(SNR), a bandwidth estimation method based on Distributed Compressive Sensing(DCS) is proposed, which uses the joint sparse characteristics of multiple LFM signals with the same modulation type from the same source to estimate the bandwidth of LFM signal. Firstly, the under-sampled LFM signal model is constructed. Secondly, the LFM bandwidth is reconstructed by DCS algorithm. Then the parameter estimation ability of the proposed method under low SNR conditions is analyzed. Finally, the feasibility and validity of the proposed method are verified by simulation.