Paper Info
Title
Sparse models and sparse recovery for ultra-wideband SAR applications
Abstract
This paper presents a simple yet very effective time-domain sparse representation and associated sparse recovery techniques that can robustly process raw data-intensive ultra-wideband (UWB) synthetic aperture radar (SAR) records in challenging noisy and bandwidth management environments. Unlike most previous approaches in compressed sensing for radar in general and SAR in particular, we take advantage of the sparsity of the scene and the correlation between the transmitted and received signal directly in the raw time domain even before attempting image formation. Our framework can be viewed as a collection of practical sparsity-driven preprocessing algorithms for radar applications that restores and denoises raw radar signals at each aperture position independently, leading to a significant reduction in the memory requirement as well as the computational complexity of the sparse recovery process. Recovery results from real-world data collected by the U.S. Army Research Laboratory (ARL) UWB SAR systems illustrate the robustness and effectiveness of our proposed framework on two critical applications: 1) recovery of missing spectral information in multiple frequency bands and 2) adaptive extraction and/or suppression of radio frequency interference (RFI) signals from SAR data records.
Year
DOI
Venue
2014
10.1109/TAES.2014.120454
Aerospace and Electronic Systems, IEEE Transactions  
Keywords
Field
DocType
Synthetic aperture radar,Ultra wideband radar,Radar imaging,Apertures,Noise,Image reconstruction
Continuous-wave radar,Radar,Radar engineering details,Computer vision,Radar imaging,Synthetic aperture radar,Sparse approximation,Inverse synthetic aperture radar,Electronic engineering,Bistatic radar,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
50
2
0018-9251
Citations 
PageRank 
References 
7
0.58
20
Authors
3
Name
Order
Citations
PageRank
Lam Nguyen17714.56
Trac D. Tran21507108.22
Thong T. Do323412.76