ArticleSensors (Basel, Switzerland)2018
WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Effortless 3D radio maps generation for fingerprinting-based indoor positioning system.Scientific reports · 2025Article
- A Novel Non-Contact Multi-User Online Indoor Positioning Strategy Based on Channel State Information.Sensors (Basel, Switzerland) · 2024Article
- Automated Room-Level Localisation Using Building Plan Information.Sensors (Basel, Switzerland) · 2024Article
- A novel device-free Wi-Fi indoor localization using a convolutional neural network based on residual attention.PeerJ. Computer science · 2024Article
- Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- Research on Indoor 3D Positioning Algorithm Based on WiFi Fingerprint.Sensors (Basel, Switzerland) · 2022Article
- Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting.Sensors (Basel, Switzerland) · 2022Article
- A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting.Sensors (Basel, Switzerland) · 2022Article
- Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review.Sensors (Basel, Switzerland) · 2022Review
- MHSA-EC: An Indoor Localization Algorithm Fusing the Multi-Head Self-Attention Mechanism and Effective CSI.Entropy (Basel, Switzerland) · 2022Article
- Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi.Sensors (Basel, Switzerland) · 2022Article
- WiFi Fingerprinting Indoor Localization Based on Dynamic Mode Decomposition Feature Selection with Hidden Markov Model.Sensors (Basel, Switzerland) · 2021Article
- Ultra-Wideband Positioning Sensor with Application to an Autonomous Ultraviolet-C Disinfection Vehicle.Sensors (Basel, Switzerland) · 2021Article
- A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting.Sensors (Basel, Switzerland) · 2021Article
- Fingerprinting-Based Indoor Localization Using Interpolated Preprocessed CSI Phases and Bayesian Tracking.Sensors (Basel, Switzerland) · 2020Article
- Kullback-Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information.Sensors (Basel, Switzerland) · 2019Article
- Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area.Sensors (Basel, Switzerland) · 2019Article
- Time-Series Laplacian Semi-Supervised Learning for Indoor LocalizationSensors (Basel, Switzerland) · 2019Article
- Novel WiFi/MEMS Integrated Indoor Navigation System Based on Two-Stage EKF.Micromachines · 2019Article
- A Deep Learning Approach to Position Estimation from Channel Impulse Responses.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy workload on fingerprint storage. Thus, this paper presents a novel Random Forest fingerprinting localization (RFFP) method using channel state information (CSI), which utilizes the Random Forest model trained in the offline stage as fingerprints in order to economize memory space and possess a good anti-multipath characteristic. Furthermore, a series of specific experiments are conducted in a microwave anechoic chamber and an office to detail the localization performance of RFFP with different wireless channel circumstances, system parameters, algorithms, and input datasets. In addition, compared with other algorithms including K-Nearest-Neighbor (KNN), Weighted K-Nearest-Neighbor (WKNN), REPTree, CART, and J48, the RFFP method provides far greater classification accuracy as well as lower mean location error. The proposed method offers outstanding comprehensive performance including accuracy, robustness, low workload, and better anti-multipath-fading.
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