mlsanityData to 7 Oct 2026

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

arXiv 2205.14935 ↗First posted 30 May 2022cs.LG eess.SPcited by 8

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 39 of its 192 references that are in the map. First posted on arXiv 30 May 2022.

Builds on

39 of 192 references.

References · 39

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27 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 3; 2026: 1.

8 papers.

  1. 2023ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimationcited by 3
  2. 2024SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networkscited by 3
  3. 2023CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Networkcited by 2
  4. 2025Transforming Indoor Localization: Advanced Transformer Architecture for NLOS Dominated Wireless Environments with Distributed Sensorscited by 2
  5. 2024Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioningcited by 1
  6. 2025Conformal Prediction for Indoor Positioning with Correctness Coverage Guaranteescited by 1
  7. 2025Permutation-Invariant Transformer Neural Architectures for Set-Based Indoor Localization Using Learned RSSI Embeddingscited by 0
  8. 2026Learning Displacement-Aware WiFi Representations for Weakly Supervised Relative Localizationcited by 0