Abstract
Specific emitter identification (SEI) is a well-established approach to providing precise target information for civilian and military applications. For most deep learning (DL) based SEI schemes, neural operators directly learn mappings from the raw baseband waveform or its transformed representation. Different from existing schemes, we propose a novel complex Fourier neural operator (CFNO) in this letter, which introduces a time and frequency domain attention mechanism. With the CFNO block, features are fully learned from different domain perspectives. We evaluate the proposed method based on the joint distortion model of the transmitter and compare it with several state-of-the-art SEI algorithms. Simulation results demonstrate its excellent performance, making the CFNO block a good candidate for extracting fingerprints.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
