Advancements in noise reduction for wheel speed sensing using enhanced LSTM models.

This research presents an Enhanced Long Short-Term Memory (LSTM) deep learning model for robust noise reduction in automotive wheel speed sensors. While wheel speed sensors are pivotal to vehicle stability, high-intensity or non-stationary noise often degrades their performance. Traditional filtering methods, including adaptive approaches and basic digital signal processing, frequently underperform under complex conditions. The proposed model addresses these limitations by incorporating an attention mechanism that selectively emphasizes transient high-noise frames, preserving essential rotational information. Comprehensive experiments, supported by Variational Mode Decomposition (VMD) and the Hilbert-Huang Transform (HHT), demonstrate that the Enhanced LSTM surpasses conventional techniques and baseline LSTM architectures in suppressing interference. T results yield significantly improved metrics across varying noise intensities, confirming both efficacy and stability. Although factors such as computational cost and the need for extensive labeled data remain, the Enhanced LSTM shows strong potential for real-time applications in wheel speed sensing. This work offers valuable insights into advanced noise mitigation and serves as a foundation for future deep learning research in complex automotive signal processing tasks.