Summary
International Symposium on Antennas and Propagation
2013
Session Number:FA-P
Session:
Number:FA-P-23
Radar HRRP Adaptive Denoising via Sparse and Redundant Representations
Min Li, Gongjian Zhou, Bin Zhao, Taifan Quan,
pp.-
Publication Date:2013/10/22
Online ISSN:2188-5079
Summary:
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 proposed for obtaining denoised HRRP without requiring any knowledge about the noise statistics. As a modification to OMP-CV, OMP-MCV modifies 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.