Environment Adaptive Localization Method Using Wi-Fi and Bluetooth Low Energy

Received signal strength indicator (RSSI) based fingerprinting techniques for indoor positioning can be readily implemented via a wireless access point. These methods have therefore been widely studied in the field of positioning. However, fingerprinting suffers low accuracy of positioning on account of high noise occurrences which are caused by other wireless communication signals and environmental factors when the RSSI is received, and by relatively high errors on account of low position resolution compared to other methods such as time of flight and inertial navigation technology. In this paper, a modified fingerprint algorithm based on Wi-Fi and Bluetooth low energy applied to the log-distance path loss model is proposed to remove unnecessary Wi-Fi data, and produce the AP database that can be updated depending on the changes of the ambient environment as the indoor area is increasingly complicated and extended. Instead of using the existing fingerprinting techniques of consulting signal strengths as factors that are stored in a database, the proposed algorithm employs environmental variables to which the log-distance path loss model is applied. Therefore, the proposed algorithm has higher position resolution than existing fingerprint and can improve the accuracy of positioning because of its low dependence on reference points. To minimize database and eliminate inaccurate AP signals, the Hausdorff distance algorithm and median filter are applied. Using a database in which environment variables are stored, the results are inversely transformed into the log-distance path loss model for expression as coordinates. The proposed algorithm was compared with existing fingerprinting methods. The experimental results demonstrated the reduction of positioning improvement by 0.695 m from 2.758 to 2.063 m.