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GauPro: An Accuracy-Improved Indoor Positioning System Based on Beacon Probabilistic Fingerprint

With the increasing demand for indoor location based services and the fast growth of related technologies, indoor positioning services are becoming more and more popular and important. Multiple approaches have been studied in this field for the past two decades, but the performance is still not as satisfying as the outdoor positioning services. In this research, we study several mainstream indoor localisation technologies and propose GauPro, which is a Beacon proba-bilistic fingerprint algorithm that utilises the Gaussian Kernel model to improve the accuracy of traditional algorithm. Based on GauPro, we design and implement an indoor positioning system that can be easily deployed without any hardware modification of the receiving-end devices thereby providing positioning services for general smartphone users. For the evaluation, we deploy the system in a work space and set up two different field experiment scenarios. The first scenario is a rooms and corridors area and the second one is an empty hall area. The experimental results show that our GauPro algorithm-based system outperforms the traditional Bayesian probability-based approach by 17.3% in terms of error distance.