Blind Calibration for Acoustic Vector Sensor Arrays

Conference Paper (2018)
Author(s)

K. Nambur Ramamohan (Microflown Technologies, Arnhem, TU Delft - Signal Processing Systems)

SP Chepuri (TU Delft - Signal Processing Systems)

Daniel Fernandez Comesaña (Microflown Technologies, Arnhem)

Graciano Carrillo Pousa (Microflown Technologies, Arnhem)

Geert J.T. Leus (TU Delft - Signal Processing Systems)

Research Group
Signal Processing Systems
Copyright
© 2018 K. Nambur Ramamohan, S.P. Chepuri, Daniel Fernandez Comesana, Graciano Carrillo Pousa, G.J.T. Leus
DOI related publication
https://doi.org/10.1109/ICASSP.2018.8462035
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 K. Nambur Ramamohan, S.P. Chepuri, Daniel Fernandez Comesana, Graciano Carrillo Pousa, G.J.T. Leus
Research Group
Signal Processing Systems
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
3544-3548
ISBN (print)
978-1-5386-4659-5
ISBN (electronic)
978-1-5386-4658-8
Reuse Rights

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Abstract

In this paper, we present a calibration algorithm for acoustic vector sensors arranged in a uniform linear array configuration. To do so, we do not use a calibrator source, instead we leverage the Toeplitz blocks present in the data covariance matrix. We develop linear estimators for estimating sensor gains and phases. Further, we discuss the differences of the presented blind calibration approach for acoustic vector sensor arrays in comparison with the approach for acoustic pressure sensor arrays. In order to validate the proposed blind calibration algorithm, simulation results for direction-of-arrival (DOA) estimation with an uncalibrated and calibrated uniform linear array based on minimum variance distortion less response and multiple signal classification algorithms are presented. The calibration performance is analyzed using the Cramér-Rao lower bound of the DOA estimates.

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