关键词: 室内定位, 设备异构性, 最强AP分类, 标准化指纹, 极限学习机

Abstract: To solve the problem of positioning accuracy deviation and poor robustness for indoor positioning based on WiFi fingerprints, causing by the heterogeneity of devices, a calibration-free indoor location method is proposed for across heterogeneous devices. The raw fingerprint database is standardized, combining the strongest AP classification and Procrustes Analysis. Training classification standardized fingerprints by using ELM（Extreme Learning Machine） method, a classified regression model for RSS（Received Signal Strength） and location is obtained, then it gets the positioning location. In the typical laboratory building, four heterogeneous types of mobile phones are used for experiments. The experimental results show that the proposed method improves the accuracy and stability of positioning, compared with traditional calibration free methods.

Key words: indoor location, heterogeneity of devices, strongest AP classification, standardized fingerprint, extreme learning machine