上海交通大学智能微波光波融合创新中心（imLic）硕士生邓安逸同学的工作— High-resolution ISAR imaging based on photonic receiving for high-accuracy automatic target recognition的相关研究成果近期已发表在Optics Express杂志上（DOI: 10.1364/OE.457443），该工作得到了国家重点研发计划、国家自然科学基金等的资助。该研究团队利用宽带光学模数转换系统实现了对Ka波段宽带雷达的射频直采接收机，验证了该接收方式能有效提高逆合成孔径雷达成像的分辨率，同时通过该方式构建不同样本的逆合成孔径图像数据集，结合深度学习模型，验证了高分辨率的图像能有效提升雷达自动目标识别的性能，解决逆合成孔径图像相较于光学图像可识别性较差的问题。
摘要:A scheme of high-resolution inverse synthetic aperture radar (ISAR) imaging based on photonic receiving is demonstrated. In the scheme, the linear frequency modulated (LFM) pulse echoes with 8 GHz bandwidth at the center frequency of 36 GHz are directly sampled with the photonic analog-to-digital converter (PADC). The ISAR images of complex targets can be constructed without detection range swath limitation due to the fidelity of the sampled results. The images of two pyramids demonstrate that the two-dimension (2D) resolution is 3.3 cm × 1.9 cm. Furthermore, the automatic target recognition (ATR) is employed based on the high-resolution experimental dataset under the assistance of deep learning. Despite of the small training dataset containing only 50 samples for each model, the ATR accuracy of three complex targets is still validated to be 95 % on a test dataset with the equal number of samples.