DOA Estimation with a Rank-deficient Covariance matrix: A Regularized Least-squares approach
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionElectrical and Computer Engineering Program
Physical Science and Engineering (PSE) Division
Date
2021-01-18Permanent link to this record
http://hdl.handle.net/10754/667633
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Show full item recordAbstract
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.Citation
Ali, H., Ballal, T., Al-Naffouri, T. Y., & Sharawi, M. S. (2020). DOA Estimation with a Rank-deficient Covariance matrix: A Regularized Least-squares approach. 2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium). doi:10.23919/usnc/ursi49741.2020.9321628Conference/Event name
2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium)ISBN
978-1-7281-6197-6Additional Links
https://ieeexplore.ieee.org/document/9321628/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9321628
ae974a485f413a2113503eed53cd6c53
10.23919/USNC/URSI49741.2020.9321628