SAP dissimilarity based high performance Wi-Fi indoor localization

There are two longstanding issues: the fluctuation of wireless signal and the unstability of Access Point (AP), which greatly affect the performance of Wi-Fi based indoor localization. Most existing fingerprint based Wi-Fi localization methods adopt machine learning or data mining algorithms to get the location information; however, they ignore some intrinsic factors. According to massive observations, we discover some underlying characteristics of Wi-Fi indoor localization from the view of signal strength and AP. Hence, a new dissimilarity based localization method SAP (Signal-AP) is proposed to implement high performance indoor localization. The results show that SAP not only improves the localization accuracy, but also has desirable scalability of environment.

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