NDLI: A bayesian approach to multiple target localization

Content Provider	IEEE Xplore Digital Library
Author	Bai, Er-wei Dasgupta, Soura Mudumbai, Raghuraman
Copyright Year	2015
Description	Author affiliation: Department of Electrical and Computer Engineering, University of Iowa, 52242, United States (Bai, Er-wei; Dasgupta, Soura; Mudumbai, Raghuraman)
Abstract	In this paper a multiple target localization problem is considered with only a partially known signal propagation model. Specifically, we assume that localization is to be effected by measuring the received signal strength (RSS) at each sensor. That RSS is modeled by a standard signal propagation model, though with unknown parameters. We adopt a Bayesian approach to propose a Markov Chain Monte Carlo (MCMC) type of algorithm for simultaneously estimating these unknown parameters and the source locations. Our approach also yields a posterior density function of these quantities conditioned on the RSS measurements. Such a density is useful for a visual inspection of the terrain to ascertain the source locations. The convergence of the algorithm is established under mild assumptions. Simulation results that support the analysis are provided.
Starting Page	2426
Ending Page	2431
File Size	255243
Page Count	6
File Format	PDF
e-ISBN	9781479978861
DOI	10.1109/CDC.2015.7402571
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-15
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Position measurement Density functional theory Convergence Bayes methods Markov processes Monte Carlo methods Upper bound
Content Type	Text
Resource Type	Article

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