WiFi FTM and Map Information Fusion for Accurate Positioning
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WiFi-based positioning has recently drawn attention since the Fine Timing Measurement protocol was defined as part of the 802.11 standard. This protocol allows very accurate range measurements based on Round Trip Time estimation. In this paper we discuss the challenges of evaluating performance, present a ground truth acquiring tool and analyze results. We further discuss the problem of obtaining a reliable position estimation given the unique nature of the measurement error. We argue that a standard Kalman Filter (KF) has some shortcomings that can be overcome by using additional information sources. The more general Bayesian Filter (BF) offers a method for integrating map information with any other nonlinear information, to provide a more accurate position estimation. Keywords—WiFi FTM, Fine Timing Measurement, Indoor Positioning, RTT, ToF, localization, Bayesian Filter, Kalman Filter, Map Matching, Smoothing
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