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.

最近，我们目睹了使用WiFi，蓝牙和其他技术的室内定位应用采用了不同的机器学习技术。这些技术的范围从启发式派生的基于手工特征的传统机器学习算法，特征选择算法到分层自演化的基于特征的深度学习算法。WiFi /蓝牙指纹数据的瞬态和混乱性质以及不同设备配置的不同信号灵敏度带来了许多挑战，这些挑战影响了野外室内定位系统的性能。本文旨在提供有关机器学习技术的全面的最新技术调查，这些技术最近已用于本地化目的。因此，我们回顾了机器学习技术在该领域的适用性，以及基本的本地化原理，应用以及与现有系统相关的潜在问题和挑战。我们还阐述了最新的进展和最先进的机器学习技术，以可视化室内定位研究领域中可能的未来方向。