Indoor Positioning Systems: Technologies and Selection Strategies

Khairi Abdulrahim; Nurin Sabrina Muhamad Khairi; Azzam Abdul Rahman

doi:10.33102/mjosht.410

Authors

Khairi Abdulrahim Department of Electronic and Electrical Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, 71800, Nilai, Malaysia.
Nurin Sabrina Muhamad Khairi Department of Electronic and Electrical Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, 71800, Nilai, Malaysia.
Azzam Abdul Rahman Department of Electronic and Electrical Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, 71800, Nilai, Malaysia.

DOI:

https://doi.org/10.33102/mjosht.410

Keywords:

Indoor Positioning Systems, UWB, RFID, Computer Vision, BLE, WiFi, ZigBee, PDR

Abstract

Indoor Positioning Systems (IPS) have emerged as essential technologies for achieving accurate localization and navigation within enclosed environments where satellite-based systems, such as the Global Navigation Satellite System (GNSS), are unreliable. This article provides a comprehensive overview of the major IPS technologies, highlighting their operating principles, advantages, and limitations. The study examines a diverse range of positioning methods, including computer vision-based systems with dynamic tracking capabilities, pedestrian dead reckoning (PDR) solutions that function independently of external infrastructure, and communication signal–based approaches such as Ultra-Wideband (UWB), Radio Frequency Identification (RFID), Bluetooth Low Energy (BLE), Wi-Fi, and ZigBee. Each technology demonstrates distinct performance characteristics in terms of accuracy, cost efficiency, scalability, and energy consumption. By systematically comparing these approaches, this work identifies the contexts in which each technology performs optimally and discusses the trade-offs associated with their implementation. Furthermore, the paper synthesizes recent advancements that integrate artificial intelligence, machine learning, and sensor fusion techniques to enhance positioning precision and robustness under complex indoor conditions. The findings of this review aim to assist researchers, engineers, and practitioners in selecting the most appropriate IPS solution for specific application domains, facilitating informed decision-making in designing effective and reliable indoor localization systems.

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