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Joint ML calibration and DOA estimation with separated arrays

Abstract : This paper investigates parametric direction-of-arrival (DOA) estimation in a particular context: i) each sensor is characterized by an unknown complex gain and ii) the array consists of a collection of subarrays which are substantially separated from each other leading to a structured noise covariance matrix. We propose two iterative algorithms based on the maximum likelihood (ML) estimation method adapted to the context of joint array calibration and DOA estimation.Numerical simulations reveal that the two proposed schemes, the iterative ML (IML) and the modified iterative ML (MIML) algorithms for joint array calibration and DOA estimation, outperform the state of the art methods and the MIML algorithm reaches the Cramér-Rao bound for a low number of iterations.
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Submitted on : Tuesday, June 28, 2016 - 11:21:49 AM
Last modification on : Wednesday, November 3, 2021 - 9:34:42 AM
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Virginie Ollier, Mohammed Nabil El Korso, Remy Boyer, Pascal Larzabal, Marius Pesavento. Joint ML calibration and DOA estimation with separated arrays. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2016, Shanghai, China. ⟨10.1109/icassp.2016.7472227⟩. ⟨hal-01247271⟩



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