MAPS: Indoor Localization Algorithm Based on Multiple AP Selection

Abstract

In recent years, indoor fingerprint-based localization algorithm has been widely used by applications on smart phone. In these localization algorithms, it’s very popular to use WiFi signal characteristics to represent the location fingerprint. With the fast popularization of WiFi, the WiFi access points (APs) could be seen everywhere. However, as the number of APs increases, the dimension of the fingerprint and the complexity of fingerprint-based localization algorithm subsequently increase. Responding to the above challenges, this paper proposes a novel indoor localization algorithm MAPS (indoor localization algorithm based on multiple access point selection). MAPS could effectively reduce the complexity of localization computation, and improve the performance of localization through AP selection method. With the first round AP selection, MAPS can obtain a stable subset of AP, thus reducing the dimension of fingerprint, and obtaining better discrimination. And with the second round of AP selection, AP subset could be further condensed to construct a decision tree in each location cluster. This step can further improve the localization performance. The experimental results shown, as compared with classical indoor localization algorithm, MAPS has better positioning accuracy, and could achieve the accuracy of over 90% within 2m location error.