Improved indoor localization system based on virtual access points in a Wi-Fi environment by filtering schemes

Positioning services are increasingly used for applications such as navigation, advertising and social media. While outdoor navigation based on GPS and/or cellular systems works well, indoor navigation is a much tougher challenge. This paper proposes an indoor localization using Wi-Fi received signal strength indicator (RSSI) measurements from existing access points. Using RSSI data from multiple access points, additional access points will be added in the system thru linear regression statistical model to create virtual access points (VAPs). This paper fuses the low-cost and flexible system of fingerprint localization with VAP creation and the improvement in accuracy by adapting Kalman Filter (KF) and Particle Filter (Pf). The effectiveness of using KF with VAP has demonstrated positive results in previous research. This paper aims to integrate the use of PF in the system. To improve the performance of indoor localization, the proposed model will be an improved virtual access point localization, proposed filtering (VAP+KF+PF).