Optimization of Fingerprints Reporting Strategy for WLAN Indoor Localization - 2018


This Project investigates a way to optimize the fingerprints reporting strategy to enhance localization accuracy, and how the optimal strategy theory will be utilised to streamline the planning of WLAN fingerprinting localization systems. In particular, we 1st reveal that the fingerprints reporting drawback is actually an NP-Hard size-constrained supermodular maximization problem, and then show the inapplicability of the state-of-the-art approximation algorithms to the problem. We then propose a new algorithm and show that if the number of fingerprints measurements is giant enough, then the localization accuracy is at most one - e times worse than the optimal worth, with e any given constant close to 0. Moreover, we have a tendency to demonstrate how the optimal strategy theory can be utilized to improve accuracy of location estimation by resolving the difficulty of comparable fingerprints for both faraway and close-by locations, with an iterative algorithm developed to cross check fingerprints sampled in different locations, in order to derive the most effective attainable result of localization. More, we tend to reveal the connection between accuracy of location estimation and coverage of Wi-Fi signals in indoor areas when planning deployment of APs. Experiment results are presented to validate our theoretical analysis.

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