上海交通大学智能微波光波融合创新中心（imLic）硕士生郭航的工作——Automatic target recognition based on receiver optimization of photonic time-stretched coherent radar（基于优化后的光子时间拉伸接收机的自动目标识别）的相关成果近期被Optics Letters期刊接收发表，该工作得到了国家重点研发计划（2019YFB2203700）、国家自然科学基金（61822508）的部分资助。该工作提出了一种基于改进的光子时间拉伸相干雷达 (PTS-CR) 的自动目标识别 (ATR) 方案：通过使用具有高重复率的光脉冲，并增加接收机中第一段色散介质的色散量，PTS-CR的接收孔径可以覆盖整个探测范围。同时使用具有不同拉伸因子的两个通道来恢复信号延迟信息，仿真和实验结果验证了新方案的可行性。最后，基于改进的接收方案，PTS-CR 成功地对放置在旋转台上的四个不同目标进行了 ATR。结合卷积神经网络（CNN）的训练，识别准确率为94.375%。
摘要: Abstract—We demonstrate an automatic target recognition (ATR) scheme based on an improved photonic time-stretched coherent radar (PTS-CR). The reception apertures of the PTS-CR can cover the entire detection range by receiving the echo signal with high repetition rate pulses and increasing the amount of dispersion of the first dispersive medium in the receiver. Two channels with different stretching factors are simultaneously used to restore the signal delay information. Simulated and experimental results verify the feasibility of the new scheme. Finally, based on the improved receiving scheme, PTS-CR successfully performed ATR on four different targets placed on a rotating stage. Combining this with the training of the convolutional neural network (CNN), the recognition accuracy rate is 94.375%.