Share Email Print
cover

Proceedings Paper

Distinguishing ability analysis of compressed sensing radar imaging based on information theory model
Author(s): Hai Jiang; Bingchen Zhang; Yueguan Lin; Wen Hong; Yirong Wu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Recent theory of compressed sensing (CS) has been widely used in many application areas. In this paper, we mainly concentrate on the CS in radar and analyze the distinguishing ability of CS radar image based on information theory model. The information content contained in the CS radar echoes is analyzed by simplifying the information transmission channel as a parallel Gaussian channel, and the relationship among the signal-to-noise ratio (SNR) of the echo signal, the number of required samples, the length of the sparse targets and the distinguishing level of the radar image is gotten. Based on this result, we introduced the distinguishing ability of the CS radar image and some of its properties are also gotten. Real IECAS advanced scanning two-dimensional railway observation (ASTRO) data experiment demonstrates our conclusions.

Paper Details

Date Published: 27 October 2011
PDF: 8 pages
Proc. SPIE 8179, SAR Image Analysis, Modeling, and Techniques XI, 81790S (27 October 2011); doi: 10.1117/12.897595
Show Author Affiliations
Hai Jiang, The National Key Lab. of Science and Technology on Microwave Imaging (China)
National Astronomical Observatories (China)
Institute of Electronics (China)
Bingchen Zhang, The National Key Lab. of Science and Technology on Microwave Imaging (China)
Institute of Electronics (China)
Yueguan Lin, The National Key Lab. of Science and Technology on Microwave Imaging (China)
National Disaster Reduction Ctr. of China (China)
Wen Hong, The National Key Lab. of Science and Technology on Microwave Imaging (China)
Institute of Electronics (China)
Yirong Wu, The National Key Lab. of Science and Technology on Microwave Imaging (China)
Institute of Electronics (China)


Published in SPIE Proceedings Vol. 8179:
SAR Image Analysis, Modeling, and Techniques XI
Claudia Notarnicola; Simonetta Paloscia; Nazzareno Pierdicca, Editor(s)

© SPIE. Terms of Use
Back to Top