Comparative Analysis on WiFi-based Indoor Positioning Techniques

With the proliferation of smartphones and artificial intelligence, WiFi-based indoor positioning techniques has diversified and advanced significantly. Fingerprinting techniques utilize RSSI from WiFi Access Points to estimate indoor locations. However, due to the sensitivity of WiFi signals to environmental changes, we face challenges of rebuilding fingerprint maps whenever the environment undergoes changes. Artificial intelligence techniques overcome these limitations of fingerprinting methods and enhance the accuracy of indoor positioning. Ultimately, artificial intelligence techniques improve the performance of indoor positioning techniques compared to traditional statistical methods. This paper aims to explore the performance of various artificial intelligence algorithms applied to indoor positioning and discuss how to improve the performance of indoor positioning algorithms