FMA-RRSS: Fingerprint Matching Algorithm Based on Relative Received Signal Strength in Indoor Wi-Fi Positioning

Indoor positioning based on Wi-Fi signal attracts a lot of attention in the location field. However, accuracy of positioning is often affected due to the instability of indoor Wi-Fi signal. In order to overcome this problem and reduce the errors, we present a new indoor WiFi-based positioning algorithm called FMA-RRSS which is the abbreviation of Fingerprint Matching Algorithm Based on Relative Received Strength Signal. In this paper, we propose a new position-determined model to produce location information from WiFi signal. Combined with the traditional solution of fingerprint database in indoor positioning, we use relative RSS value representing location information to estimate a clients actual position. We set up a positioning system and implement the FMA-RRSS in the actual WiFi environment. The experimental results show the improvement of positioning accuracy, fingerprint training complexity and response time of matching algorithm compared with other traditional indoor positioning algorithm like RADAR and Free Loc.