New research focuses on enhancing indoor localisation using Bluetooth Low Energy (BLE) technology by addressing challenges in signal instability and noise. The authors propose a system combining the Kalman filter for signal smoothing and deep learning models, specifically Autoencoders and Convolutional Autoencoders, for feature extraction from Received Signal Strength Indicator (RSSI) data. The method uses a fingerprinting approach, collecting data in two phases, offline for creating a reference database and online for matching new measurements to predict locations.
The study demonstrates that integrating the Kalman filter with the Convolutional Autoencoder model yields an average localisation error of 0.98 metres, significantly improving accuracy. Experimental comparisons with existing methods highlight the proposed system’s effectiveness in balancing cost, energy efficiency, and precision. The findings suggest this approach as a robust solution for indoor localisation in environments requiring high accuracy and low energy consumption.