NDLI: Directions of arrival estimation by learning sparse dictionaries for sparse spatial spectra

Content Provider	IEEE Xplore Digital Library
Author	Cheng-Yu Hung Jimeng Zheng Kaveh, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Minnesota, Minneapolis, MN, USA (Cheng-Yu Hung; Jimeng Zheng; Kaveh, M.)
Abstract	A major limitation of most methods exploiting sparse signal or spectral models for the purpose of estimating directions-of-arrival stems from the fixed model dictionary that is formed by array response vectors over a discrete search grid of possible directions. In general, the array responses to actual DoAs will most likely not be members of such a dictionary. In this work, the sparse spectral signal model with uncertainty of linearized dictionary parameter mismatch is considered, and the dictionary matrix is reformulated into a multiplication of a fixed base dictionary and a sparse matrix. Based on this double-sparsity model, an alternating dictionary learning-sparse spectral model fitting approach is proposed to reduce the estimation errors of DoAs and their powers. Group-sparsity estimator and Lasso-based Least Squares are utilized in the formulation of the associated optimization problem. The performance of the proposed methods are demonstrated by numerical simulations.
Starting Page	377
Ending Page	380
File Size	214417
Page Count	4
File Format	PDF
ISBN	9781479914814
ISSN	2151870X
DOI	10.1109/SAM.2014.6882420
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Direction-of-arrival estimation Vectors Optimization Arrays Dictionaries Estimation Mathematical model
Content Type	Text
Resource Type	Article

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