基于压缩感知的低数据率雷达采样与成像方法

传统的信号获取体制要求采样率大于两倍信号带宽,这使得高速率A/D转换成为经典超宽带高分辨雷达系统的瓶颈技术之一。压缩感知理论提供了一种低速率采样的信号精确采集和重构方式。本文基于压缩感知理论,提出一种新的雷达采样与成像方法。根据目标的散射特性,采用了基于小波变换的雷达目标稀疏表示方法;结合雷达成像原理,构造了基于Fourier束的最优测量矩阵。仿真实验表明,基于压缩感知的低数据率雷达采样与成像方法,能在数据率仅为传统系统数据率15%的条件下,获得良好的成像结果,尤其是能对弱小目标进行高分辨成像。本文所提的方法可为新体制高分辨率成像雷达系统的设计提供支持。

Radar Sampling and Imaging Based on Compressive Sensing Method with Low Data Rate

The classical signal acquisition systems require the sampling rate to be larger than twice the bandwidth of the signal.This makes the very high rate analog to digital conversion become a bottleneck in the design of modern high resolution radar systems.Compressive Sensing theory provides us with a new approach for exact signal acquiring and recovery with low data rate.This paper proposes a new approach for radar sampling and imaging based on compressive sensing.According to the characteristics of the target’s radar scattering,sparse representation of the scattering coefficients is established by using wavelet transform.Moreover,the Fourier ensemble based compressive sampling matrix is designed with the consideration of the SAR imaging principle.Finally,the presented method is validated by numerical simulation,where a better imaging result,especially for the weak target high resolution imaging,is obtained with only 15% data rate compared to the conventional imaging methods.The radar sampling and imaging method presented in this paper can support the development of high resolution radar imaging systems.