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

Cheng Yu Hung, Jimeng Zheng, Mostafa Kaveh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014
PublisherIEEE Computer Society
Pages377-380
Number of pages4
ISBN (Print)9781479914814
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014 - A Coruna, Spain
Duration: Jun 22 2014Jun 25 2014

Other

Other2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014
CountrySpain
CityA Coruna
Period6/22/146/25/14

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    Hung, C. Y., Zheng, J., & Kaveh, M. (2014). Directions of arrival estimation by learning sparse dictionaries for sparse spatial spectra. In 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014 (pp. 377-380). [6882419] IEEE Computer Society. https://doi.org/10.1109/SAM.2014.6882420