Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Estimation Problems in Hybrid Systems
Estimation Problems in Hybrid Systems
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
CASA '03 Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA 2003)
Emerging Technologies of Augmented Reality
Emerging Technologies of Augmented Reality
Enabling Mobile Phones To Support Large-Scale Museum Guidance
IEEE MultiMedia
Performance comparison of EKF and particle filtering methods for maneuvering targets
Digital Signal Processing
Analysis of parallelizable resampling algorithms for particle filtering
Signal Processing
Minimising the effect of WiFi interference in 802.15.4 wireless sensor networks
International Journal of Sensor Networks
Navitime: Supporting Pedestrian Navigation in the Real World
IEEE Pervasive Computing
Statistical learning theory for location fingerprinting in wireless LANs
Computer Networks: The International Journal of Computer and Telecommunications Networking
Beyond location based: the spatially aware mobile phone
W2GIS'06 Proceedings of the 6th international conference on Web and Wireless Geographical Information Systems
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
An improvement to the interacting multiple model (IMM) algorithm
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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In this paper we address the problem of indoor tracking using received signal strength (RSS) as a position-dependent data measurement. 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. Although various models have been proposed in the literature, they often require the use of very large collections of data in order to fit them and display great sensitivity to changes in the radio propagation environment. In this work we advocate the use of switching multiple models that account for different classes of target dynamics and propagation environments and propose a flexible probabilistic switching scheme. The resulting state-space structure is termed a generalized switching multiple model (GSMM) system. Within this framework, we investigate two types of models for the RSS data: polynomial models and classical logarithmic path-loss representation. The first model is more accurate however it demands an offline model fitting step. The second one is less precise but it can be fitted in an online procedure. We have designed two tracking algorithms built around a Rao-Blackwellized particle filter, tailored to the GSMM structure and assessed its performances both with synthetic and experimental measurements.