Bayesian localization in wireless networks using angle of arrival

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.