1D-CNN for non-cooperative rotational Doppler signal extraction

The discovery of the rotational Doppler effect (RDE) has opened new opportunities for detecting parameters of rotating targets. In recent years, the physical mechanisms underlying this effect have been thoroughly investigated. However, existing methods for extracting target rotation rates remain largely confined to conventional spectral analysis techniques like Fourier transformation. In this study, we propose a machine learning-based approach for automatic rotation rate extraction, which enables rapid and accurate measurement under conditions which misalignment exists between vortex beam axis and the target rotating axis. This method significantly simplifies the rotation rate retrieval process while maintaining high precision. Furthermore, we provide an in-depth investigation into the intrinsic mechanisms of the algorithm, uncovering new physical insights that pave the way for practical applications of this technology.