Abstract
WiFi offers simple, convenient, ubiquitous, and economic solutions for indoor positioning services, by matching a pre-established WiFi’s RSSI fingerprint database to a mobile terminal’s received RSSI values. A setback of this fingerprint matching method is its low precision, only miserably on order of meters, due to signal impairment by indoor complicated environment. To circumvent this, we revise the traditional weighted K-neighborhood algorithm by incorporating a Bayesian probability optimization. The proposed combination of Bayesian with weighted K-neighborhood algorithm improves the accuracy and reduces the average running time. Computer simulation shows that proposed Bayesian probabilistic optimization algorithm improves the positioning accuracy from 34% to 46%, with an average of about 14.86%, and the computation stability is also enhanced.
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