Gyroscope Real-Time Denoising by an Adaptive Threshold Wavelet Algorithm: Achieving over 12 dB SNR Improvement

Gyroscopes play a pivotal role in applications ranging from navigation and robotics to aerospace and consumer electronics, where denoising is often critical to improve overall system performance. Traditional Kalman-based filters are often regarded as the gold standard for inertial sensor denoising, yet they require assumptions on the system's dynamics that may not always hold, particularly in the presence of abrupt or unpredictable maneuvers. Several alternative approaches avoid such assumptions, but typically exhibit inferior performance compared to Kalman filters (KFs). Here we report on a novel wavelet-based denoising algorithm that operates in real time without relying on prior knowledge of the sensor's dynamic conditions. Our technique adaptively calibrates the threshold by modeling noise with a generalized Gaussian distribution (GGD) and adjusts it according to the ongoing signal variance. This strategy offers two core advantages: it preserves relevant signal discontinuities and handles broad noise distributions effectively, including non-Gaussian noise. We validate the algorithm on two distinct gyroscope platforms: a state-of-the-art fiber optic gyroscope, characterized by low noise and non-Gaussian behavior, and a commercial MEMS gyroscope with primarily Gaussian noise. Standard test signals - such as blocks, step, heavisine, and Doppler - reveal that our approach surpasses the KF by up to 1 dB and outperforms alternative wavelet-based techniques by at least 4 dB in signal-to-noise ratio (SNR) enhancement. Furthermore, the algorithm exhibits minimal overshoot at signal discontinuities, ensuring accurate angular rate reconstruction. These results establish our method as a high-performance and robust solution for gyroscope denoising especially in high-end inertial sensing. The algorithm operates without any prior knowledge of the host platform's motion model; it relies only on weak, sensor-level statistical assumptions that are satisfied by practically all gyroscopes.