NDLI: Parameter estimation of coherently distributed sources using sparse representation

Content Provider	IEEE Xplore Digital Library
Author	Liang Zhou Guangjun Li Zhi Zheng Xuemin Yang
Copyright Year	2014
Description	Author affiliation: Sch. of Commun. & Inf. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China (Liang Zhou; Guangjun Li; Zhi Zheng; Xuemin Yang)
Abstract	In this paper, a new estimator of coherently distributed source employing the sparse representation technology is proposed by utilizing subspace fitting principle. The proposed method uses the eigenvalue-decomposition method on the sample covariance matrix of the sensor array received data and obtains the signal eigenvectors. We represent the generalized steering vectors of coherently distributed source containing central direction-of-arrival (DOA) and angular spread on over complete dictionaries subject to sparse constraint in subspace fitting method. Then subspace fitting problem is transformed into a sparse reconstruction problem. Finally, we use L1 norm method to solve the sparse reconstruction problem, which is optimized by the second order cone programming (SOCP) framework. Compared with the existing algorithms for coherently distributed source, such as DSPE and ESPRIT, the simulation results show that the proposed method has better resolution performance, especially in small number of snapshots.
Starting Page	577
Ending Page	581
File Size	353939
Page Count	5
File Format	PDF
ISBN	9781479954018
e-ISBN	9781479954032
DOI	10.1109/ChinaSIP.2014.6889309
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Direction-of-arrival estimation Dictionaries Parameter estimation Fitting Estimation Sparse representation Vectors Coherently distributed sources Arrays Subspace fitting principle
Content Type	Text
Resource Type	Article

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