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Collection des publications scientifiques du Centre Henri Lebesgue |  |
Communication dans un congrès
Sequential Sensor Placement using Bayesian Compressed Sensing for Source Localization
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.
https://hal.inria.fr/hal-03070390
Contributeur : Cédric Herzet <>
Soumis le : mardi 15 décembre 2020 - 18:28:52
Dernière modification le : lundi 1 février 2021 - 14:59:24
Contributeur : Cédric Herzet <>
Soumis le : mardi 15 décembre 2020 - 18:28:52
Dernière modification le : lundi 1 février 2021 - 14:59:24
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