Optimizing Indoor Localization Using RSSI and IQ Data with Machine Learning

This paper explores implementing and evaluating a Bluetooth Low Energy (BLE)-based indoor localization system using Received Signal Strength Indicator (RSSI) and Angle of Arrival (AoA) data via machine learning. A survey of localization technologies (RFID, GPS, ZigBee, and BLE) provides context on capabilities and limitations in indoor positioning. IQ data and phase-based angle estimation show how BLE 5.1’s direction-finding features enable sub-meter accuracy. A multi-phase experiment in a three-story academic building examines model performance with different tag distributions, movement patterns, and environmental constraints. Machine learning models such as Support Vector Machines and Deep Neural Networks are trained and evaluated across six phases. The best models achieve 85–92% accuracy in room-level localization despite real-world signal interference. Limitations such as generalizability and signal obstruction are additionally discussed. The study demonstrates the viability of BLE-based machine learning forscalable, semi-precise indoor localization and identifies areas for optimization and future work.