NSSC OpenIR  > 微波遥感部
Radar Imaging with Quantized Measurements Based on Compressed Sensing
Dong, Xiao; Zhang, Yunhua
Department微波遥感部
Source Publication2015 Sensor Signal Processing for Defence, SSPD 2015
2015
Language英语
ISBN9781479974443
AbstractIn this paper, we consider the problem of radar imaging with quantized data. The quantized CS (QCS) method is used to reconstruct the radar image of sparse targets from quantized data. The reconstruction problem is derived in the maximum a posteriori (MAP) estimation framework and formulated as a convex optimization problem. We compare the proposed method with the traditional l1-regularization method using 1-D simulated data with different quantization bits. For coarse quantization with 1 or 2 bits, the simulation results show that the QCS method outperforms the l1- regularization method in high SNR situations. For high- resolution quantization with more bits, we derive the conditions under which the l1-regularization method and the QCS method are equivalent. This statement is explained theoretically and confirmed by simulation results. © 2015 IEEE.
Conference Name5th Sensor Signal Processing for Defence, SSPD 2015
Conference DateSeptember 9, 2015 - September 10, 2015
Conference PlaceEdinburgh, United kingdom
Indexed ByEI ; CPCI
Document Type会议论文
Identifierhttp://ir.nssc.ac.cn/handle/122/5371
Collection微波遥感部
Recommended Citation
GB/T 7714
Dong, Xiao,Zhang, Yunhua. Radar Imaging with Quantized Measurements Based on Compressed Sensing[C],2015.
Files in This Item:
File Name/Size DocType Version Access License
201507288520.pdf(1084KB)会议论文 开放获取CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Dong, Xiao]'s Articles
[Zhang, Yunhua]'s Articles
Baidu academic
Similar articles in Baidu academic
[Dong, Xiao]'s Articles
[Zhang, Yunhua]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Dong, Xiao]'s Articles
[Zhang, Yunhua]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.