Radar HRRP Adaptive Denoising via Sparse and Redundant Representations

摘要：

　　We address the radar high resolution range profile(HRRP) denoising problem for improving the recognition rate of HRRP at low signal-to-noise ratio (SNR).Gaussian white noise in HRRP return is suppressed by an approach based on sparse representation.A Fourier redundant dictionary is established for sparsely representing HRRP returns.An adaptive signal recovering algorithm, Orthogonal Matching Pursuit-Modified Cross Validation (OMP-MCV), is propoed for obtaining denoised HRRP without requiring any knowledge about the noise statistics.As a modification to OMP-CV, OMP-MCV modifiles the cross validation iteration condition, which can prevent the iteration procedure from terminating at local minimum impacted by noise.Simulation results show that OMP-MCV achieves better performance than OMP-CV and some other traditional denoising method, like discrete wavelet transform, for HRRP returns denoising.

作者: Min Li Gongjian Zhou Bin Zhao Taifan Quan

作者单位: School of Electronics and Information Engineering Harbin Institute of Technology Harbin, 150001, China

会议类型: 国际会议

会议名称: 2013 International Symposium on Antennas and Propagation(2013天线与传播国际会议)

会议地点: 南京

会议语种:英文

页码: 1094-1097

在线出版日期: 2013-10-23（万方平台首次上网日期，不代表论文的发表时间）