ORIGINAL RESEARCH article
Front. Phys.
Sec. Optics and Photonics
1D-CNN for Non-cooperative Rotational Doppler Signal Extraction
Provisionally accepted
- National Key Laboratory of Space Integrated Information System, Beijing, China
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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.
Keywords: CNN - convolutional neural network, machine learning (ML), measuremetn techniques, optical vortex, Rotational Doppler Effect (RDE), rotational speed
Received: 09 Oct 2025; Accepted: 03 Dec 2025.
Copyright: © 2025 Qiu, XIONG, ZHENG and WANG. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Song Qiu
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