Joint ML calibration and DOA estimation with separated arrays
Virginie Ollier, Mohammed Nabil El Korso, Rémy Boyer, Pascal Larzabal, Marius Pesavento
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 Cramer-Rao bound for a low number of iterations.
BibTeX
@inproceedings{icassp2016_jointmlcalibrati,
title = {Joint ML calibration and DOA estimation with separated arrays},
author = {Virginie Ollier and Mohammed Nabil El Korso and Rémy Boyer and Pascal Larzabal and Marius Pesavento},
booktitle = {ICASSP 2016},
year = {2016}
}