A dynamic system approach for radio location fingerprinting in wireless local area networks
IEEE Transactions on Communications
Adaptive radio maps for pattern-matching localization via inter-beacon co-calibration
Pervasive and Mobile Computing
The WiMap: A Dynamic Indoor WLAN Localization System
International Journal of Advanced Pervasive and Ubiquitous Computing
Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem
Pervasive and Mobile Computing
Theoretical entropy assessment of fingerprint-based Wi-Fi localization accuracy
Expert Systems with Applications: An International Journal
IEEE 802.15.4a CSS-based mobile object locating system using sequential Monte Carlo method
Computer Communications
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Indoor positioning is an enabling technology for delivery of location-based services in mobile computing environments. This paper proposes a positioning solution using received signal strength in indoor Wireless Local Area Networks. In this application, an explicit measurement equation and the corresponding noise statistics are unknown because of the complexity of the indoor propagation channel. To address these challenges, we introduce a new state-space Bayesian filter: the Nonparametric Information (NI) filter. This filter effectively tracks motion in situations where the Kalman filter and its variants are inapplicable, while maintaining a computational complexity comparable to that of the Kalman filter. To deal with the noisy nature of the indoor propagation environment, the NI filter is used in the design of an intelligent dynamic WLAN tracking system. The system anticipates future position values and adapts its sensing and estimation parameters accordingly. Our experimental results conducted on measurements from a real office environment indicate that the combination of the intelligent design and the NI filter results in significant improvements over the Kalman and particle filters.