Observatorio de I+D+i UPM

Descripción
We propose using a Bayes factor sequential hypothesis test for the decision fusion problem in wireless sensor networks, in which several sensors send a report to a fusion center so that a global decision is taken. This problem is frequently modeled in current literature as a hypothesis test from Bernoulli samples. We propose using a sequential composite hypothesis test based on Bayes Factor using Beta distributions as prior distributions. We obtain closed form expressions for the distributions, which allows us to develop a very efficient algorithm to implement our approach. When we validate our approach via simulations, we observe that, when compared to the common counting rule, our algorithm provides a lower average error and requires a smaller number of samples to make a decision.
Internacional	Si
Nombre congreso	2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Tipo de participación	970
Lugar del congreso	Cannes
Revisores	Si
ISBN o ISSN	978-1-5386-6528-2
DOI	10.1109/SPAWC.2019.8815408
Fecha inicio congreso	02/07/2019
Fecha fin congreso	05/07/2019
Desde la página	1
Hasta la página	5
Título de las actas	2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Esta actividad pertenece a memorias de investigación

• Autor: Juan Parras Moral (UPM)
• Autor: Santiago Zazo Bello (UPM)

• Creador: Centro o Instituto I+D+i: Centro de I+d+i en Procesado de la Información y Telecomunicaciones
• Grupo de Investigación: Grupo de Aplicaciones del Procesado de Señal (GAPS)
• Departamento: Señales, Sistemas y Radiocomunicaciones