Ensemble-Based Location Tracking Using Passive RFID

Location tracking of passive RFID tags is useful for its ultra-low cost, but is very challenging for its passive nature. A passive RFID tag relies on no internal power source and draws power from the field created by the reader to power the microchip's circuits. This has made passive RFID tags highly sensitive to surrounding materials, as well as any disturbance. Therefore, conventional machine learning models may not perform well. In this paper, we propose an ensemble-based machine learning model, together with novel feature engineering techniques, for location tracking of passive RFID, which can work seamlessly with any supervised learning methods. The reader-based sub-models training method used in our model significantly reduces the training time by splitting our model into smaller sub-models and training them in parallel, which is desirable in real-world application.