In order to realize precise positioning and navigation of people in complex indoor environment where satellite signals cannot be received

an indoor positioning method based on multi-source information fusion is proposed. The indoor pedestrian dead reckoning（PDR） is collected by integrating the direction sensor data of smart phones. WiFi and Bluetooth signal information in indoor environment for signal strength matching and correction of PDR cumulative error. For the case of low precision of WiFi fingerprint positioning in complex indoor environments

a WiFi fingerprint localization algorithm combines random forest（RF） classification and improved K nearest neighbor（KNN） algorithm

reduces computational complexity and improves positioning precision. Based on the improved WiFi fingerprint localization algorithm and multi-source information fusion analysis

the method of particle filtering and map constraint is proposed to further improve the positioning precision. The experimental results show that the path estimated by the multi-source information fusion localization method combined with particle filtering and map constraint is the closest to the real path

which proves the effectiveness of the proposed method.