Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

By Suining He, S. Chan. IEEE Communications Surveys and Tutorials, 2016

1,264 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ca6261d8ab678d9f973fce5aaa5d29f9c25c6bd2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Pavel Davidson
2. Rui Zhang
3. Xuyu Wang
4. C. Laoudias
5. David Gesbert
6. Shuguang Cui
7. Shiwen Mao
8. Emil Björnson

Citations in the corpus

• CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach (2016, background): “Indoor localization has attracted considerable interest in the research community with many interesting prior works [18], [19].”
• The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications (2024, unknown): “CSI-fingerprinting positioning is an alternative to geometric approaches that can provide enhanced performance in LOS and NLOS scenarios [132], [133], [134].”
• Indoor Positioning Systems Based on Visible Light Communication: State of the Art (2017, background): “In addition, the growing commercial interest in Indoor Location-Based Services (ILBS) has spurred the development of indoor positioning technique [1].”
• Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “Wireless indoor localization has been previously reviewed [23], [67]–[76].”
• A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G (2023, methodology): “It is hoped that such tutorial discussions would inspire more follow-up works to unlock the full potential of CKM for realizing the paradigm shift from the conventional environment-unaware networks to future environment-aware networks.”
• Recurrent Neural Networks for Accurate RSSI Indoor Localization (2019, methodology): “Beside the probabilistic approach, the deterministic methods use a similarity metric to differentiate the measured signal and the fingerprint data in the dataset to locate the user’s position [11].”
• A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation (2018, methodology): “Another focus of [20] is in-depth reviews on Pedestrian Dead-Reckoning (PDR) algorithms, classified into walk detection and step counting, step length estimation, and walking direction estimation.”
• A Survey of Selected Indoor Positioning Methods for Smartphones (2017, background): “He and Chan [20] assumed that a major concern with fingerprint dimension reduction proposed in [27] is that the selected APs and their weights derived from the offline training samples may be not optimal during online positioning because the environment dynamically changes.”