Deep learning technology has been widely used in synthetic aperture radar automatic target recognition (SAR ATR) tasks due to its good performance and high efficiency. However, the development of semiconductor technology cannot completely satisfy the demand for electronic hardware with high computational capability to conduct deep neural networks. To solve this dilemma, we propose an optronic convolutional neural network (OPCNN) that can perform SAR ATR tasks directly in optical platform. In OPCNN, all computational operations are implemented in optics with high speed and low energy cost. Electronic platforms are used only to control devices and transmit data information without a massive computational burden. As in digital CNNs, the convolutional layer, downsampling layer, nonlinear activation layer, and fully connected layer are all contained in OPCNN. The simulations demonstrate the feasibility of our OPCNN in solving SAR ATR problems. The good performance in experiments, which achieves 87.4% and 93.8% recognition accuracy on original and denoised moving and stationary target acquisition and recognition dataset, validates the application ability of OPCNN in practical scenario. In one-time recognition tasks, the processing time of our OPCNN is only 0.26 s with the speed of light and the power consumption of our prototype is also far less than digital processer, which is 954 W. Through analysis, our OPCNN obtains the higher processing speed and lower energy cost than digital CNNs with the same structure due to the advantages of optical technology. Also, the scalability of optical structure contributes to build more complex networks to solve complicated dataset without the demand of advanced electronic hardware.

中文翻译：

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用于SAR自动目标识别的光电卷积神经网络

深度学习技术以其良好的性能和高效率在合成孔径雷达自动目标识别（SAR ATR）任务中得到了广泛的应用。然而，半导体技术的发展并不能完全满足对具有高计算能力的电子硬件进行深度神经网络的需求。为了解决这个难题，我们提出了一种光电卷积神经网络（OPCNN），可以直接在光学平台上执行 SAR ATR 任务。在 OPCNN 中，所有的计算操作都是在光学中实现的，速度快，能耗低。电子平台仅用于控制设备和传输数据信息，没有大量的计算负担。与数字 CNN 一样，卷积层、下采样层、非线性激活层、和全连接层都包含在 OPCNN 中。模拟证明了我们的 OPCNN 在解决 SAR ATR 问题方面的可行性。良好的实验性能，在原始和去噪移动和静止目标采集识别数据集上分别达到了 87.4% 和 93.8% 的识别准确率，验证了 OPCNN 在实际场景中的应用能力。在一次性识别任务中，我们的OPCNN处理时间仅为光速0.26 s，并且我们的原型机的功耗也远低于数字处理器954 W。通过分析，我们的OPCNN获得了更高的由于光学技术的优势，处理速度快，能耗比相同结构的数字CNN低。还，