Conference proceeding
A bayesian approach to multiple target localization
2015 54th IEEE Conference on Decision and Control (CDC), Vol.54, pp.2426-2431
12/2015
DOI: 10.1109/CDC.2015.7402571
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.
Details
- Title: Subtitle
- A bayesian approach to multiple target localization
- Creators
- Er-wei Bai - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USASoura Dasgupta - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USARaghuraman Mudumbai - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2015 54th IEEE Conference on Decision and Control (CDC), Vol.54, pp.2426-2431
- Publisher
- IEEE
- DOI
- 10.1109/CDC.2015.7402571
- ISSN
- 0743-1546
- eISSN
- 2576-2370
- Language
- English
- Date published
- 12/2015
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984083820002771
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