2009
Achutegui, Katrin; Martino, Luca; Rodas, Javier; Escudero, Carlos J; Miguez, Joaquin
A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data Artículo en actas
En: 2009 IEEE International Conference on Control Applications, pp. 1702–1707, IEEE, Saint Petersburg, 2009, ISBN: 978-1-4244-4601-8.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian methods, Control systems, Filtering algorithms, generalized interacting multiple model, GIMM, indoor radio, Indoor tracking, mobile radio, mobile terminal, Monte Carlo methods, multipath propagation, position-dependent data measurement, random process, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, Sliding mode control, State-space methods, state-space model, Target tracking, tracking, transmitter-to-receiver distance, wireless network, wireless technology
@inproceedings{Achutegui2009,
title = {A Multi-Model Particle Filtering Algorithm for Indoor Tracking of Mobile Terminals Using RSS Data},
author = {Katrin Achutegui and Luca Martino and Javier Rodas and Carlos J Escudero and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5280960},
isbn = {978-1-4244-4601-8},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Control Applications},
pages = {1702--1707},
publisher = {IEEE},
address = {Saint Petersburg},
abstract = {In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.},
keywords = {Bayesian methods, Control systems, Filtering algorithms, generalized interacting multiple model, GIMM, indoor radio, Indoor tracking, mobile radio, mobile terminal, Monte Carlo methods, multipath propagation, position-dependent data measurement, random process, random processes, Rao-Blackwellized sequential Monte Carlo tracking, received signal strength, RSS data, Sliding mode control, State-space methods, state-space model, Target tracking, tracking, transmitter-to-receiver distance, wireless network, wireless technology},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. This type of measurements is very appealing because they can be easily obtained with a variety of wireless technologies which are relatively inexpensive. The extraction of accurate location information from RSS in indoor scenarios is not an easy task, though. Since RSS is highly influenced by multipath propagation, it turns out very hard to adequately model the correspondence between the received power and the transmitter-to-receiver distance. The measurement models proposed in the literature are site-specific and require a great deal of information regarding the structure of the building where the tracking will be performed and therefore are not useful for a general application. For that reason we propose the use of a compound model that combines several sub-models, whose parameters are adjusted to specific and different propagation environments. This methodology, is called interacting multiple models (IMM), has been used in the past for modeling the motion of maneuvering targets. Here, we extend its application to handle also the uncertainty in the RSS observations and we refer to the resulting state-space model as a generalized IMM (GIMM) system. The flexibility of the GIMM approach is attained at the expense of an increase in the number of random processes that must be accurately tracked. To overcome this difficulty, we introduce a Rao-Blackwellized sequential Monte Carlo tracking algorithm that exhibits good performance both with synthetic and experimental data.