Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review

TL;DR

This review paper discusses how deep learning has advanced indoor positioning using fingerprinting, highlighting benefits, challenges, datasets, and future research directions in this rapidly evolving field.

Contribution

It provides a comprehensive analysis of deep learning techniques in indoor fingerprinting, comparing methods, datasets, and discussing challenges and future trends.

Findings

Deep learning significantly improves fingerprinting accuracy.

Various datasets are available for indoor positioning research.

Challenges include data quality and implementation pitfalls.

Abstract

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.