Abstract
Indoor positioning has emerged as a widely used application of Wi-Fi wireless networks. A region-based fingerprinting approach is presented for indoor positioning in Wi-Fi wireless networks. This proposed method compares the fingerprint of a Wi-Fi tag with that of a region-based group of reference points, instead of an individual reference point. With the fingerprinting position estimate obtained, and with an inertial measurement unit integrated with the Wi-Fi tag, a stochastic system model is adopted to track the target's position when it is in piecewise constant velocity motion in Wi-Fi wireless networks. The stochastic system model utilizes Wi-Fi fingerprinting position estimates as measurements and inertial sensing data as control inputs. Both simulation studies and experiment data have shown the positioning performance of the integrated mobile platform with improved accuracy, by using the proposed Wi-Fi and inertial sensing technologies.
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