A Survey of Machine Learning for Indoor Positioning

Abstract

<p>Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-of-sight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning.</p>

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“…RSS belongs to the non-geometric technique since it is common to deploy it along with fingerprinting procedures. Usually, RSS-based localization techniques involve two phases: an offline phase and an online phase [7]. In the off-line phase, there is the definition of a so-called radio map in which every point of the space is associated with a given power level.…”

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“…We also have the Bayesian filtering, the maximum a posteriori (MAP), the maximum likelihood, Gaussian filter, Viterbi algorithm, and least-squares algorithm, which can improve the system's qualities. There are several types of machine learning [10]: unsupervised learning, supervised learning, and reinforcement learning. For example, supervised kth-nearest neighbor (K-NN) learning is a simple and effective machine learning (ML) [11].…”

“…If the number of reference points remains large in each group after clustering overfitting problem is likely to occur. Despite the positive impact of machine learning algorithms on indoor location systems, they still face some challenges and limitations, such as [10]:…”