Abstract
WiFi-based indoor localization has been deeply studied and researched due to the convenient deployment of the WLAN infrastructure. However, indoor localization based on WiFi suffers the fluctuation of Received Signal Strength (RSS), including missing RSS during collection, redundant Access Points (APs), and abnormal RSS values. Therefore, in this paper, we propose a hybrid filtering algorithm based on isolated forest and weighted mean to ensure the real-time filtering correction of RSS signal and provide high-quality fingerprint signal for online location. In addition, a two-panel fingerprint homogeneity graph is adopted to gauge the resemblance of localization fingerprints, and the estimated 2-D location is predicted by the integration of panel results. We have conducted experiments to demonstrate the localization algorithm’s performance. Compared with other algorithms, the results show that the proposed method can achieve the best performance.
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