RFID-based Indoor Positioning using Edge Machine Learning

Indoor positioning of objects and people is becoming of great importance in the Internet of Things (IoT), in-home automation, and navigation in malls, airports, or very large buildings. Positioning is determined by multiple distance measurements between reference points and sensors. Distance measurement uses the time of flight of an ultrasonic signal traveling from an emitter to receiving sensors. This requires close synchronization between the emitter and the sensors and a sharp time resolution of the time of arrival (TOA) of the ultrasonic signal. Usually, TOA is detected using cross-correlation processing requiring significant computational resources at the sensors level. In this work, the synchronization is done using the RFID standard protocol features. The TOA detection is performed firstly by training off-line a Machine Learning model using as input the peaks indexes of the ultrasonic signal received and the output of a cross-correlation-based positioning system, as ground-truth. In a second phase, the positioning is evaluated and tested on-board using the previously trained model on a microcontroller. The system architecture is presented and experimental results on the positioning accuracy are shown accordingly. Results show a mean positioning error below 25 cm in 95% of the positionings in a typical room.