Performance Analysis of Fingerprint Matching Algorithms for Indoor Localization

Abstract

Localization using Wi-Fi received signal strength indication (RSSI) signals gives accurate user position results for indoor localization when the RSSI signals from Wi-Fi access points (APs) cover the entire localization area. The most popular localization algorithm used in the Wi-Fi RSSI signal based localization systems is the Wi-Fi fingerprinting and it uses different fingerprint matching algorithms for user position estimation. In this paper, we propose a comparative analysis of different fingerprint matching algorithms for Wi-Fi RSSI signal based localization systems. In the analysis, we used nearest neighbour (NN), k-nearest neighbors algorithm (kNN), weighted $k$ -nearest neighbour (wkNN) and Bayesian fingerprint matching algorithms for user position estimation. The performance of these fingerprint matching algorithms is discussed in terms of average localization error and probability distribution of localization error. The experiment results show that the wkNN fingerprint matching algorithm gives high position accuracy as compared to other fingerprint matching algorithms. The results from the NN fingerprint matching algorithm has high localization error and is not suitable for Wi-Fi RSSI signal based localization systems.