DOA Estimation with a Rank-deficient Covariance matrix: A Regularized Least-squares approach

Hussain Ali, Tarig Ballal, Tareq Y. Al-Naffouri, Mohammad S. Sharawi

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

Abstract

DOA estimation in the presence of coherent sources using a small number of snapshots faces the challenge of rank deficiency of the received signal covariance matrix. When the covariance matrix is rank deficient, only the pseudo inverse of the covariance matrix can be computed, which can give undesirable results. Traditionally, regularized least-squares (RLS) algorithms are used to tackle estimation problems in systems with ill-conditioned or rank deficient matrices. In this work, we combine the Capon beamformer with the RLS framework to develop a DOA estimation method for scenarios with rank deficient covariance matrices. Simulation results demonstrate the effectiveness of the proposed approach.
Original languageEnglish (US)
Title of host publication2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium)
PublisherIEEE
Pages87-88
Number of pages2
ISBN (Print)978-1-7281-6197-6
DOIs
StatePublished - Jan 18 2021

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