A Survey of Machine Learning Techniques for Indoor Localization and Navigation Systems

Abstract

In the recent past, we have witnessed the adoption of different machine learning techniques for indoor positioning applications using WiFi, Bluetooth and other technologies. The techniques range from heuristically derived hand-crafted feature-based traditional machine learning algorithms, feature selection algorithms to the hierarchically self-evolving feature-based Deep Learning algorithms. The transient and chaotic nature of the WiFi/Bluetooth fingerprint data along with different signal sensitivity of different device configurations presents numerous challenges that influence the performance of the indoor localization system in the wild. This article is intended to offer a comprehensive state-of-the-art survey on machine learning techniques that have recently been adopted for localization purposes. Hence, we review the applicability of machine learning techniques in this domain along with basic localization principles, applications, and the underlying problems and challenges associated with the existing systems. We also articulate the recent advances and state-of-the-art machine learning techniques to visualize the possible future directions in the research field of indoor localization.