Indoor object localization based on WiFi signals has been researched increasingly in the last decade. Most of reported works concentrate on (1) building an optimal model for relationship between WiFi signal features and the object positions or (2) applying a fingerprinting method for learning and matching the best position candidates. In this paper, we proposed a new combined method which uses both of above approaches for WiFi-based object localization system. From our robust path-loss model, the distances between mobile user and APs (Access Points) are calculated. A new radio map of distance features instead of RSSI (Received Signal Strength Indicator) values is defined in order to make the radio map independent from the WiFi receivers. The matching methods of KNN and SVM are applied to estimate the mobile user position. Some comparative experiments on life environment are conducted and promising results are gained on our proposed system.
Keyword
Dataset, Multi-modal system, Localization, Identification, Camera network, WiFi
