Title :
Time-Domain Dictionary for Sparse Representation of Radar High Resolution Range Profile
Author :
JinRong Zhong ; GongJian Wen ; Cong Hui Ma ; Boyuan Ding
Author_Institution :
ATR Lab., Nat. Univ. of Defense Technol., Changsha, China
Abstract :
The Dictionary is the most important basis of sparse representation which is the key issue of sparse component analysis and compressed sensing. Most of the existing dictionaries are constructed in frequency domain. This paper presented a novel dictionary constructed in time domain for radar signal representation based on geometrical theory of diffraction (GTD) model. Since Radar signal is distributed non-uniformly in time domain, the low-energy part of time responses can be cut-off with insignificant energy loss. As a result, the time-domain dictionary (TD) can be viewed as a sparse matrix, which can save memory and reduce computation complexity greatly, compared with frequency domain dictionaries. Finally, experimental results demonstrate the effectiveness and superiority of the time-domain dictionary.
Keywords :
compressed sensing; computational complexity; geometrical theory of diffraction; radar resolution; signal representation; sparse matrices; time-domain analysis; GTD model; TD; compressed sensing; computation complexity reduction; energy loss; geometrical theory of diffraction model; memory saving; radar high resolution sparse signal representation; radar signal distribution; sparse component analysis; sparse matrix; time response; time-domain dictionary; Dictionaries; Frequency-domain analysis; Indexes; Matching pursuit algorithms; Radar; Sparse matrices; Time-domain analysis; radar signal; sparse representation; time-domain dictionary;
Conference_Titel :
Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-7980-6
DOI :
10.1109/CSE.2014.363