Research on Time Difference Prediction of RTD Fluxgate Sensors Based on an Improved Transformer Neural Network

Accurately predicting time-difference signals is a key prerequisite for extracting effective temporal characteristics from the output of RTD fluxgate sensors and enhancing the measurement reliability of the sensors. However, the nonlinear characteristics and temporal dependencies of residence time difference (RTD)-fluxgate time-difference signals make accurate prediction difficult. In this study, an improved Transformer neural network model is proposed for RTD-fluxgate time-difference signals. By introducing positional encoding, the proposed method incorporates temporal position information into the Transformer model, enabling effective extraction of temporal features from sensor output signals. To alleviate overfitting during model training, dropout and weight decay strategies are introduced to improve the generalization capability of the prediction model. The predicted time-difference signals are analyzed and the key temporal features are extracted for sensor signal processing applications. The proposed method is compared with feedforward neural networks and long short-term memory networks. Experimental results obtained under the same constant magnetic field demonstrate that the proposed method improves cosine similarity (CS) by 37% and 4%, and reduces mean square error (MSE) by 29% and 2%, respectively. The results verify the effectiveness of the proposed approach for RTD-fluxgate time-difference signal prediction and provide a data-driven method for sensor signal analysis and performance evaluation in magnetic measurement applications. This method provides a novel approach for predicting the time-difference signals of RTD fluxgate sensors, offering a reliable reference for subsequent signal processing and improving the accuracy and consistency of magnetic field data.