%0 Conference Proceedings %T Sequential Sensor Placement using Bayesian Compressed Sensing for Source Localization %+ Equipe Models and AlgoriThms for pRocessIng and eXtracting information (Lab-STICC_MATRIX) %+ École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne) %+ SIMulation pARTiculaire de Modèles Stochastiques (SIMSMART) %+ Laboratoire Modélisation et Sûreté des Systèmes (LM2S) %A Courcoux-Caro, Milan %A Vanwynsberghe, Charles %A Herzet, Cédric %A Baussard, Alexandre %< avec comité de lecture %B EUSIPCO 2020 - 28th European Signal Processing Conference %C Amsterdam, Netherlands %I IEEE %3 European Signal Processing Conference %V 2021 %P 241-245 %8 2021-01-18 %D 2021 %R 10.23919/Eusipco47968.2020.9287709 %K Acoustic %K source localization %K sequential sensor placement %K compressed sensing %Z Engineering Sciences [physics]/Signal and Image processingConference papers %X 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. %G English %2 https://inria.hal.science/hal-03070390/document %2 https://inria.hal.science/hal-03070390/file/0000241.pdf %L hal-03070390 %U https://inria.hal.science/hal-03070390 %~ UNIV-BREST %~ INSTITUT-TELECOM %~ ENSTA-BRETAGNE %~ UNIV-RENNES1 %~ IRMAR %~ UR2-HB %~ CNRS %~ INRIA %~ UNIV-TROYES %~ UNIV-UBS %~ INSA-RENNES %~ INRIA-RENNES %~ IRISA %~ ENSTA-BRETAGNE-STIC %~ INRIA_TEST %~ UNAM %~ TESTALAIN1 %~ IRMAR-STAT %~ ENIB %~ LAB-STICC %~ CHL %~ INRIA2 %~ UR1-HAL %~ UR1-MATH-STIC %~ UR1-UFR-ISTIC %~ AGREENIUM %~ UNIV-RENNES2 %~ TEST-UR-CSS %~ UNIV-RENNES %~ INRIA-RENGRE %~ INRIA-300009 %~ INSA-GROUPE %~ UTT %~ INSTITUTS-TELECOM %~ TEST-HALCNRS %~ UR1-MATH-NUM %~ LAB-STICC_MATRIX %~ LAB-STICC_DMID %~ UTT-LIST3N %~ UTT-FULL-TEXT %~ LM2S-UTT %~ INSTITUT-AGRO