Indoor fingerprinting algorithm for room level accuracy with dynamic database

Received Signal Strength (RSS) fingerprinting based WiFi localization systems has been widely studied in recent years for indoor scenarios, whose accuracy is closely related to the reliability of the database. To attain satisfactory performance, the database needs to be updated timely to compensate for changes in the environment. In this work, we propose a rank fingerprinting based localization system, in which the radio map only takes account of the most consistent access points (APs) with most meaningful information, and uses the rank method instead of the signal strength with clustering the most correlated reference point to estimate the position. In order to keep a reasonable size of the radio map, a dimension control strategy has been proposed. The proposed algorithm deals with manifold devices to achieve high room-level accuracy in a shopping mall. It has been compared with two present algorithms, the rank based fingerprinting (RBF) algorithm and the support vector machine (SVM) based algorithm, to show its better performance.