Abstract
Various techniques have been developed for Indoor Positioning Systems (IPS), a method that fingerprints the Received Signal Strength (RSS) of WiFi at specific places that can achieve high accuracy of about one meter at the exact location. A large range of indoor navigation needs and user services can be provided by using IPS, especially in unusual conditions such as being in large complex buildings or emergency healthcare needs, etc. In this paper, a framework that incorporates the probabilistic neural network (PNN) and Jensen-Bregman Divergence (JBD) is proposed. To validate our algorithm, the results were compared with PNN and kNN nearest neighbor. Where implemented inside an academic building, the algorithm results have high accuracy with an error of less than 1m distance.
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