A Systematic Review of Radio Wave Techniques for Indoor Positioning Systems

Authors

Keywords:

Indoor positioning systems, Radio Waves, Radar Sensors, Antennas, Wireless.

Abstract

Indoor human positioning has become crucial for applications such as health monitoring, security surveillance, human pose identification and rescue operations. However, achieving reliable indoor human positioning is challenging due to numerous constraints.This paper aims to compare and analyze radio waves techniques and approaches for indoor positioning,
providing a comprehensive review for human detection, positioning and activity recognition. A systematic review of the scientific literature and datasets was conducted. Four digital libraries, ACM Library Digital, IEEE Xplore, ScienceDirect and Spring Link were searched to identify results that met the selection criteria. A data extraction process was performed on the selected articles and datasets. The Parsifal platform was utilized to extract relevant information. After completing the systematic review, It was identified 26 eligible articles and extracted 11 methods for radio wave detection. The overview of indoor positioning system with radio waves was introduced. The most frequently mentioned tools in the articles for the capture stage were Radar Sensors, Wireless Sensor, and Antennas. For the processing stage, DNN Techniques, Processing Algorithms followed by Filtering, Fingerprint, Trilateration, and other machine learning algorithms formed the majority.

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Author Biographies

Emily Juliana Costa e Silva, Federal University of Maranhão - UFMA

Emily Costa is a Master’s Student in Aerospace Engineering at the Federal University of Maranhão. Bachelor’s Degree in Computer Science from the Federal University of Maranhão - UFMA. She is currently researcher at the Data Analytics & Artificial Intelligence Laboratory (DARTi Lab). Interested in recommender systems, data analysis, and machine learning applications.

Kaio Yukio Goncalves Vieira Guedes, Federal University of Maranhão - UFMA

Kaio Goncalves is an Undergraduate student in Computer Science at the Federal University of Maranhao (UFMA).He is a researcher at the DARTi Lab (Laboratory of Data Analysis and Artificial Intelligence). Conducting research in the fields of Artificial Intelligence and Signal Processing

Pedro Augusto Araújo da Silva de Almeida Nava Alves, University of São Paulo - USP

Pedro Araujo is a Phd student in Computer Science at Universidade de São Paulo (USP) on the field of Artificial Intelligence, Optimization and Neural Architecture Search. Interests includes: Artificial Intelligence, Neural Networks, Neural Architecture Search, Signal Processing, Artificial Intelligence applied to Signal Processing and Electroencephalogram Analysis.

Paulo Rogério de Almeida Ribeiro, Federal University of Maranhão - UFMA

Paulo Rogerio de Almeida Ribeiro received the Ph.D. degree in Neuroscience from the University of Tübingen (Germany) in 2015, Master degree in Mechatronics Engineering from the University of Minho (Portugal) in 2012, and the Bachelor degree in Computer Science from the Federal University of Maranhão (Brazil) in 2009. He is an Assistant Professor (tenured position), since 2016, within the Department of Computer Engineering at the Federal University of Maranhão (UFMA) in Brazil. His research interests include Robotics, Autonomous Mobile Robot, Control and Automation Systems, Data Science, Artificial intelligence, Neuroscience and Brain-Computer Interface.

Alex Oliveira Barradas Filho, Federal University of Maranhão - UFMA

Alex Oliveira has a degree in Information Systems (2006) from the Centro Universitário do Maranhao, a master’s degree (2009) and a PhD (2015) in Electrical Engineering, with an emphasis on computer science, from the Federal University of Maranhão. He did a post-doctoral internship at the Federal University of Maranhao (2015). He has professional experience (in industry and academia) as a systems analyst, manager, consultant, teacher and researcher. He is currently Adjunct Professor III at the Federal University of Maranhao and a permanent member of the Postgraduate Program in Aerospace Engineering (PPGAERO/UFMA), coordinator of the Specialization Course in Data Analysis and Artificial
Intelligence (DTED/UFMA) and vice-coordinator of PPGAERO/UFMA. He is part of the Bioeconomy, Environment, Innovation, Intelligence, Technologies, Education and Health Center (BAITES/UFMA) as an AI Manager and Senior Researcher. He leads the Data Analysis and Artificial Intelligence Laboratory
(DARTiLab) and its teams (Stellaris, Sinapse and Game Analysis). He works in the field of data analysis and artificial intelligence, with applications in biofuels, education, aerospace technologies (satellites and predictive models of ionospheric scintillation) and health.

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Published

2025-01-30

How to Cite

Costa e Silva, E. J. ., Goncalves Vieira Guedes, K. Y., Araújo da Silva de Almeida Nava Alves, P. A., de Almeida Ribeiro, P. R., & Oliveira Barradas Filho, A. (2025). A Systematic Review of Radio Wave Techniques for Indoor Positioning Systems. IEEE Latin America Transactions, 23(3), 205–215. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/9219