The cricket compass for context-aware mobile applications
Proceedings of the 7th annual international conference on Mobile computing and networking
Machine Learning
Experiments on Local Positioning with Bluetooth
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Calibration-free WLAN location system based on dynamic mapping of signal strength
Proceedings of the 4th ACM international workshop on Mobility management and wireless access
Pervasive and Mobile Computing
Information Sciences: an International Journal
Wideband powerline positioning for indoor localization
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Using Wi-Fi Signal Strength to Localize in Wireless Sensor Networks
CMC '09 Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 01
Place lab: device positioning using radio beacons in the wild
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Accurate GSM indoor localization
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Indoor Positioning Using FM Radio
International Journal of Handheld Computing Research
CONTEXT'11 Proceedings of the 7th international and interdisciplinary conference on Modeling and using context
Indoor localization using audio features of FM radio signals
CONTEXT'11 Proceedings of the 7th international and interdisciplinary conference on Modeling and using context
Proceedings of the 10th international conference on Mobile systems, applications, and services
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The position of mobile users has become highly important information in pervasive computing environments. Indoor localization systems based on Wi-Fi signal strength fingerprinting techniques are widely used in office buildings with an existing Wi-Fi infrastructure. Our previous work has proposed a solution based on exploitation of a FM signal to deal with environments not covered with Wi-Fi signal or environments with only a single Wi-Fi access point. However, a general problem of indoor wireless positioning systems pertains to signal degradation due to the environmental factors affecting signal propagation. Therefore, in order to maintain a desirable level of localization accuracy, it becomes necessary to perform periodic calibrations of the system, which is either time consuming or requires dedicated equipment and expert knowledge. In this paper, we present a comparison of FM versus Wi-Fi positioning systems and a combination of both systems, exploiting their strengths for indoors positioning. We also address the problem of recalibration by introducing a novel concept of spontaneous recalibration and demonstrate it using the FM localization system. Finally, the results related to device orientation and localization accuracy are discussed.