AUTHOR=Qiu Song , Zheng Jin , Shao Yingjiao , Xiong Minghui , Wang Qiang 
  
TITLE=1D-CNN for non-cooperative rotational Doppler signal extraction
  
JOURNAL=Frontiers in Physics
  
VOLUME=Volume 13 - 2025
  
YEAR=2025
  
URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1721662
  
DOI=10.3389/fphy.2025.1721662
  
ISSN=2296-424X
  
ABSTRACT=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.