A Dynamic RSSI Fingerprint Matching Method for Low-Cost Indoor Positioning and Tracking Based on Smartphone
Xiaodong Gong, Jingbin Liu, Sheng Yang, Zhenbing Zhang, Zheng Li, Gege Huang, Yu Bai, Xinyi Lei and Ruizhi Chen, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China
Location: Atrium Ballroom
Alternate Number 2
Location-based service (LBS) is one of the research hotspots in mobile computing era, and the basic and key of LBS is position and navigation technology. Global Navigation Satellite System (GNSS) is generally used in outdoor position and navigation because of the advantages of high positioning accuracy, fast, time-saving and efficient. However, it is sometimes difficult to receive GNSS signals indoor for the reason that the satellite signals are easily blocked. It is extremely important to find the indoor position method. As a consequence of this limitation, the pursuit for an indoor positioning system (IPS) has been ongoing. In view of the strong demand for LBS of smart phones, utilizing Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP) is a promising position and navigation solution by considering from the aspects of low-cost and high availability. In general, the positioning system based RSSI fingerprint needs to establish a fingerprint database in the early stage. However, there is a major problem that it takes too much time and cost to establish and update the fingerprint database. In this paper, a dynamic RSSI fingerprint matching algorithm is proposed, which is oriented to indoor positioning and tracking based on smart phone. The RSSI dynamic fingerprint database is established by collecting RSSI fingerprints during dynamic walking based on a self-developed platform, and then the corresponding indoor positioning and tracking are produced by using dynamic matching algorithm. In this study, we evaluate the feasibility of building low-cost indoor Positioning and Tracking system based on Smartphone utilizing dynamic RSSI fingerprint matching method via field experiment. The experimental results show: 1) The establishment time of dynamic fingerprint database is significantly shortened and the collection efficiency is improved expressively. 2) The mean positioning error is narrow down in 2 meters. 3) A dynamic RSSI fingerprint matching method for low-cost indoor positioning and tracking based on smart phone is cost effective for large scale deployments, can operate over existing Wi-Fi and Bluetooth networks, and can provide flexibility to accommodate new RSSI sensor observations. To sum up, the proposed method can greatly reduce the application cost of fingerprint positioning, progress a satisfactory performance of positioning accuracy and provide power for the promotion of fingerprint positioning.