Research Article
Bayesian Filtering Methods for Target Tracking in Mixed Indoor/Outdoor Environments
@INPROCEEDINGS{10.1007/978-3-642-29479-2_13, author={Katrin Achutegui and Javier Rodas and Carlos Escudero and Joaqu\^{\i}n M\^{\i}guez}, title={Bayesian Filtering Methods for Target Tracking in Mixed Indoor/Outdoor Environments}, proceedings={Mobile Lightweight Wireless Systems. Third International ICST Conference, MOBILIGHT 2011, Bilbao, Spain, May 9-10, 2011, Revised Selected Papers}, proceedings_a={MOBILIGHT}, year={2012}, month={10}, keywords={Bayesian filtering indoor/outdoor tracking Kalman filter particle filter switching models}, doi={10.1007/978-3-642-29479-2_13} }- Katrin Achutegui
Javier Rodas
Carlos Escudero
Joaquín Míguez
Year: 2012
Bayesian Filtering Methods for Target Tracking in Mixed Indoor/Outdoor Environments
MOBILIGHT
Springer
DOI: 10.1007/978-3-642-29479-2_13
Abstract
We propose a stochastic filtering algorithm capable of integrating radio signal strength (RSS) data coming from a wireless sensor network (WSN) and location data coming from the global positioning system (GPS) in order to provide seamless tracking of a target that moves over mixed indoor and outdoor scenarios. We adopt the sequential Monte Carlo (SMC) methodology (also known as particle filtering) as a general framework, but also exploit the conventional Kalman filter in order to reduce the variance of the Monte Carlo estimates and to design an efficient importance sampling scheme when GPS data are available. The superior performance of the proposed technique, when compared to outdoor GPS-only trackers, is demonstrated using experimental data. Synthetic observations are also generated in order to study, by way of simulations, the performance in mixed indoor/outdoor environments.