Abstract
Wireless networks with navigation capability enable mobile devices to both communicate and determine their positions. Diversity navigation employing multiple sensing technologies can overcome the limitation of individual technologies, especially when operating in harsh environments such as indoors. To characterize the diversity of navigation systems in real environments, we performed an extensive measurement campaign, where data from heterogenous sensors were collected simultaneously. The performance of Bayesian navigation algorithms, relying on the particle filter implementation, is evaluated based on measured data from ultrawideband, ZigBee, and inertial sensors. This enables us to quantify the benefits of data fusion as well as the effect of statistical mobility models for real-time diversity navigation.
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