Target recognition in synthetic aperture radar (SAR) imagery suffers from speckle noise and geometric distortion brought by the range-based coherent imaging mechanism. A new SAR target recognition system is proposed, using a SAR-to-optical translation network as pre-processing to enhance both automatic and manual target recognition. In the system, SAR images of targets are translated into optical by a modified conditional generative adversarial network (cGAN) whose generator with a symmetric architecture and inhomogeneous convolution kernels is designed to reduce the background clutter and edge blur of the output. After the translation, a typical convolutional neural network (CNN) classifier is exploited to recognize the target types in translated optical images automatically. For training and testing the system, a new multi-view SAR-optical dataset of aircraft targets is created. Evaluations of the translation results based on human vision and image quality assessment (IQA) methods verify the improvement of image interpretability and quality, and translated images obtain higher average accuracy than original SAR data in manual and CNN classification experiments. The good expansibility and robustness of the system shown in extending experiments indicate the promising potential for practical applications of SAR target recognition.

A photonic deramp receiver based on a photonic mixer for dual-band linear frequency-modulated continuous wave (LFM-CW) radar is proposed. In the receiver, a novel modulation scheme is developed, employing a dual polarization quadrature phase shift keying (DP-QPSK) modulator which contains four sub-Mach-Zehnder modulators (sub-MZMs). The echoes and reference signal of the first band are modulated to light waves on a pair of peak-biased sub-MZMs for tracking, while the echoes and reference signal of the second band are modulated on the other pair of null-biased sub-MZMs for imaging. An experimental system, operating in C-band and Ku-band with a bandwidth of 850 MHz and 3600 MHz, respectively, is demonstrated and evaluated via a series of inverse synthetic aperture radar (ISAR) imaging tests. The results verify the idea of the dual-band radar receiver, which provides a solution for receiving dual-band echo signals with a single hardware platform.