Abstract
Abstract This paper explores the application of image semantic segmentation networks in radar signal sorting. Unlike traditional methods, it does not depend on predefined parameters or prior information, so it can show enhanced adaptability in dealing with a more complex electromagnetic environment. Firstly, the pulse descriptor PDW of collected signals is encoded into corresponding sequence images, and then U-net is used to train pulse sequence images. The trained model can be applied with high accuracy in radar signal sorting. At the same time, an attention mechanism is introduced into the U-net, which improves the recognition rate of signal features.
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.
