VOR base stations for indoor 802.11 positioning
Proceedings of the 10th annual international conference on Mobile computing and networking
The limits of localization using RSS
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Using a-priori information to improve the accuracy of indoor dynamic localization
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Radiation pattern correlation for mobile robot localization in low power wireless networks
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Restarting particle filters: an approach to improve the performance of dynamic indoor localization
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
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Using existing wireless communication networks as a localization infrastructure promises enormous cost and deployment savings over specific localization infrastructures. In this work we investigate a Bayesian network approach that uses a combination of radio signal strength (RSS) to distance estimation along with angle-of-arrival (AoA) information. We characterize the resulting localization accuracy using data collected outdoors using different radios, indoor data, and simulated data. We show how the localization performance degrades in indoor environments and analyze the different sources of errors that cause this performance degradation as compared to outdoor settings. We found our network is quite sensitive to variations in the distance to signal strength, and the additional angle information had only a small impact on localization accuracy.