JOINTML CALIBRATION AND DOA ESTIMATION WITH SEPARATED ARRAYS

被引：0

作者：

Oilier, V. ^{[1
,3
]}

El Korso, M. N. ^{[2
]}

Boyer, R. ^{[3
]}

Larzabal, P. ^{[1
]}

Pesavento, M. ^{[4
]}

机构：

[1] Univ Paris Saclay, SATIE, UMR 8029, ENS Cachan, St Aubin, France

[2] Univ Paris Ouest, EA 4416, LEME, Ville Davray, France

[3] Univ Paris Sud, UMR 8506, L2S, Gif Sur Yvette, France

[4] Tech Univ Darmstadt, Commun Syst Grp, Darmstadt, Germany

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS | 2016年

关键词：

Direction-of-arrival estimation; calibration; structured noise covariance matrix; maximum likelihood; OF-ARRIVAL ESTIMATION; CRAMER-RAO BOUNDS; DIRECTION ESTIMATION;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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 iterativeML (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.

引用

页码：2996 / 3000

页数：5

共 31 条

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