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

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.

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• Very Deep Convolutional Networks for Large-Scale Image Recognition
• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response

Cited by in corpus (4)

• New trends in indoor positioning based on WiFi and machine learning: A systematic review
• OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach
• ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation
• Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning