Joint Detection of Moving Target in Video Synthetic Aperture Radar

Video Synthetic Aperture Radar (SAR) presents great potential in ground moving target detection and tracking through high frame rate and high-resolution imaging. Target Doppler energy is essential for traditional SAR Ground Moving Target Indication (SAR-GMTI), as the target shadow can also be used for detection in video SAR. However, neither of these detection methods can stand alone to achieve robust detection in video SAR, owing to the distortion or smearing of target energy and its shadow. This paper presents the processing results of airborne video SAR real data using the Faster Region-based Convolutional Neural Network (Faster R-CNN) and the traditional track association based on dual-domain joint detection as proposed in the literature. These two approaches successfully utilize the feature and space time information of target Doppler energy and shadow in the detection of a maneuvering target.