Abstract
Aiming at the problem of indoor location fingerprint matching, this paper proposes a method to realize Wireless Local Area Network (WLAN) indoor localization by using statistical hypothesis test based on joint optimization of sample capacity and Access Point (AP). This method uses Operating Characteristics (OC) function to optimize the sample capacity of fingerprint database and make full use of the Received Signal Strength (RSS) information such as sample capacity, variance and mean. In the online phase, we rely on the two-side hypothesis test method to match the real-time RSS against the fingerprint database. The proposed localization algorithm can effectively solve the problems of the blindness of offline location fingerprint collection and instability of online fingerprint matching. The experimental results verify that our method is capable of reducing the manpower and time cost, as well as ensuring the effectiveness of localization.
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