Machine learning assisted tracking of magnetic objects using quantum diamond magnetometry

Remote magnetic sensing can be used to monitor the position of objects in real-time, enabling ground transport monitoring, underground infrastructure mapping and hazardous detection. However, magnetic signals are typically weak and complex, requiring sophisticated physical models to analyze them and a detailed knowledge of the system under study, factors that are frequently unavailable. In this work, we provide a solution to these limitations by demonstrating a Machine Learning (ML) method that can be trained exclusively on experimental data, without the need of any physical model, to predict the position of a magnetic target in real-time. The target can be any object with a magnetic signal above the floor noise, and in this case we use a quantum diamond magnetometer to track variations of few hundreds of nanoteslas produced by an elevator moving along a single axis. The one-dimensional movement is a simple yet challenging scenario, resembling realistic environments such as high buildings, tunnels or train circuits, and is the first step towards building broader applications. Our ML algorithm can be trained in approximately 40 min, achieving over 80% accuracy in predicting the target's position at a rate of 10 Hz, for a positional error tolerance of 30 cm, which is a precise distance compared to the 4-meter spacing between parking levels. Our results open up the possibility to apply this ML method more generally for real-time monitoring of magnetic objects, which will broaden the scope of magnetic detection applications.