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This work addresses the fundamental issue of online channel learning in indoor localization systems. We describe a novel algorithm for unsupervised channel learning where the path loss exponent in the Non-Line-Of-Sight (NLOS) region is adapted online while positioning. The algorithm has scalable complexity and is shown to provide significant improvement over non-adaptive systems.
We describe a method for incorporating map information to the Kalman filter that is commonly used in indoor and outdoor navigation systems. The map information is provided as a measurement to the Kalman filter to ensure the consistency of the Kalman estimate. The proposed method provides huge computational saving over common map matching algorithms that use the more computationally expensive particle...
We study the non-cooperative game when many WLAN stations compete for responses from WLAN access points in order to find their positions. The main objective of the WLAN stations, which are the players in our game, is to minimize the positioning error under various constraints on power consumption and network throughput. We provide a rigorous and general formulation of the game model where we use the...
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