Compressive Sensing for Ground Penetrating Radar maging Based on Random Filtering
Sparse signals can be reconstructed from a small set of measurements basing on the theory of compressive sensing (CS), whereas the key points are the selection of the meas-urement matrix and the reconstruction algorithm. This paper presents an imaging algorithm for ground penetrating radar based on CS. The measurement matrix is selected via random filters, which can reduce the number of nonzero elements in the measurement matrix effectively. We adopt the simple orthogonal matching pursuit (OMP) algorithm to reconstruct signal with less data storage and lower computational com-plexity. Simulation results are provided to illustrate the per-formance of the proposed method.
Ground Penetrating Radar Imaging Compressive Sensing Random Filtering Orthogonal Matching Pursuit
YunQian Cao RenBiao Wu JiaXue Liu XiaoGuang Lu
Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, P.R.China
国际会议
2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)
成都
英文
1898-1901
2011-10-24（万方平台首次上网日期，不代表论文的发表时间）