基于压缩感知的模拟到信息转换研究

Research of Analog-to-Information Converter Based on Compressive Sensing

压缩感知理论突破了Nyquist采样定理的约束,提出对稀疏信号可以以远低于Nyquist采样速率进行采样,并可以通过重构算法恢复出原信号。研究了基于压缩感知的模拟到信息转换系统,该系统由调制、低通滤波器和低速率采样3个模块组成,实现高频信号的低速率采样,并通过重构算法得到原信号。对模拟到信息转换系统的结构和原理进行了详细介绍,并应用Matlab对系统进行了大量的仿真分析,该系统能稳健地从较少的采样数据中恢复原信号。

The compressive sensing theory breaks through the constraints of Nyquist sampling theorem,which proposes that sparse signals can be sampled at a rate far below the Nyquist sampling rate,and the original signals can be reconstructed by the reconstruction algorithm. An analog-to-information converter system based on compressive sensing is studied. The system consists of three parts： modulation,low pass filter and low sampling rate sampler,which may achieve the low-rate sampling of HF signal and obtain the original signal through the reconstruction algorithm. The principle of the system is described in detail and simulated by Matlab software. The system can reconstruct the original signal from less sampled data robustly.