Abstract
Moving target brings out the different position shifts and defocusing across the image sequences acquired by circular synthetic aperture radar (CSAR) due to the Doppler shift and range smear effects. In this paper, a novel moving target detection approach for single-channel CSAR is proposed based on deep neural network (DNN). A dual-channel densely connected convolutional network (DenseNet) in consideration of complex-valued information is exploited for distinguishing the ground clutter and moving target. In terms of limited CSAR measure data set available for training the DNN network, simulated moving target samples are generated and fused into the measured ones under the various motion parameters. Finally, experiments have demonstrated that the proposed DenseNet for single-channel CSAR system processes an accepted detection performance and effectively overcomes the insufficiency of the limited dataset applications.
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.
