Spatial Inference in Sensor Networks using Multiple Hypothesis Testing and Bayesian Clustering
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A4 Artikkeli konferenssijulkaisussa
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en
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5
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2019 27th European Signal Processing Conference (EUSIPCO), pp. 1-5, European Signal Processing Conference
Abstract
The problem of statistical inference in large-scale sensor networks observing spatially varying fields is addressed. A method based on multiple hypothesis testing and Bayesian clustering is proposed. The method identifies homogeneous regions in a field based on similarity in decision statistics and locations of the sensors. High detection power is achieved while keeping false positives at a tolerable level. A variant of the EM-algorithm is employed to associate sensors with clusters. The performance of the method is studied in simulation using different detection theoretic criteria.Description
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Gölz, M, Muma, M, Halme, T, Zoubir, A & Koivunen, V 2019, Spatial Inference in Sensor Networks using Multiple Hypothesis Testing and Bayesian Clustering. in 2019 27th European Signal Processing Conference (EUSIPCO). European Signal Processing Conference, IEEE, pp. 1-5, European Signal Processing Conference, Coruna, Spain, 02/09/2019. https://doi.org/10.23919/EUSIPCO.2019.8902986