Ultra-Wideband Trained Artificial Neural Networks for Bluetooth Proximity Detection in Small Crowded Areas

Estimating the distance between indoor users is increasingly important in unexpected ways. One specific example is the need for electronic contact tracing demonstrated during the recent global pandemic. Smartphones are now routinely equipped with Bluetooth Low Energy radios, among other sensors, and these can be used for proximity detection based on received signal strength that is subject to errors due to poor modelling of the indoor propagation environment. Some high-end smartphones have now also been equipped with ultra-wideband ranging radios that provide a much more precise range measurement. This thesis demonstrates the concept of using a limited number of UWB-equipped smartphones to gather data to train Artificial Neural Networks (ANN) to improve short-range distance estimation among Bluetooth users. The trained RSSI to range model can be used for proximity determination by other Bluetooth users in small, crowded areas. Two ANN algorithms were trained using RSSI measurements from three BLE advertising channels and UWB range as ground truth and training data. The initial training and testing were conducted in a semi-empty office laboratory with 2130 observations. The RF model used 1917 samples (90% of data) for training and 213 samples (10%) for testing, while the CNN method used 1704 samples (80% of data) for training and 426 samples (20%) for evaluation. The trained neural network models were tested in two other office environments under different user conditions. The results indicate that the ANN models can estimate proximity in a new environment without further training with a mean error of less than 1.2 metres, within a range of up to 6 metres at line-of-sight (LOS). In highly constrained non-line-of-sight (NLOS) areas in the first office room, the proposed models provided proximity accuracy better than 2.9 metres. Furthermore, during testing across two adjacent office environments, each containing a single BLE device with complex furniture arrangements, the ANN models showed the proximity between the BLE devices with an error of less than 2-3 metres