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Sequential Sensor Placement using Bayesian Compressed Sensing for Source Localization

Milan Courcoux-Caro 1 Charles Vanwynsberghe 1 Cédric Herzet 2 Alexandre Baussard 3
1 Lab-STICC_ENSTAB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 SIMSMART - SIMulation pARTiculaire de Modèles Stochastiques
IRMAR - Institut de Recherche Mathématique de Rennes, Inria Rennes – Bretagne Atlantique
Abstract : This paper deals with the sensor placement problem for an array designed for source localization. When it involves the identification of a few sources, the compressed sensing framework is known to find directions effectively thanks to sparse approximation. The present contribution intends to provide an answer to the following question: given a set of observations, how should we make the next measurement to minimize (some form of) uncertainty on the localization of the sources? More specifically, we propose a methodology for sequential sensor placement inspired from the "Bayesian compressive sensing" framework introduced by Ji et al. Our method alternates between a step of sparse source localization estimation, and a step to choose the sensor position that minimizes the covariance of the estimation error. Numerical results show that an array designed by the proposed procedure leads to better performance than sensors positioned at random.
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https://hal.inria.fr/hal-03070390
Contributor : Cédric Herzet <>
Submitted on : Tuesday, December 15, 2020 - 6:28:52 PM
Last modification on : Friday, January 8, 2021 - 3:43:03 AM

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  • HAL Id : hal-03070390, version 1

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Milan Courcoux-Caro, Charles Vanwynsberghe, Cédric Herzet, Alexandre Baussard. Sequential Sensor Placement using Bayesian Compressed Sensing for Source Localization. 28th European Signal Processing Conference (EUSIPCO 2020), Jan 2021, Amsterdam, Netherlands. ⟨hal-03070390⟩

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