Data-Driven Localization of Magneto-Mechanical Resonators from Time Signals
Magneto-mechanical resonators (MMRs) are a recently introduced sensor technology for tracking and sensing applications that enables effective miniaturization down to the millimeter regime. However, estimating sensor parameters such as position from raw signals remains computationally challenging, because it requires solving a nonlinear inverse problem with computationally demanding objective function evaluations. In this work, we show how a deep learning method can accelerate this process by directly learning the inverse mapping from the measured signals. We consider the localization problem as an example and show that a convolutional-transformer regressor trained on simulated data with recorded noise can generalize to experimental data. The method achieves a median Euclidean distance error of 7 mm, which is comparable to the size of the sensor and corresponds to 6% of the size of a single coil. These results suggest that deep learning is a promising approach for rapid estimation of MMR parameters.