High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

Change detection in synthetic aperture radar images based on unsupervised artificial immune systems

In this paper, we propose a novel change detection method for synthetic aperture radar images based on unsupervised artificial immune systems. After generating the difference image from the multitemporal images, we take each pixel as an antigen and build an immune model to deal with the antigens. By continuously stimulating the immune model, the antigens are classified into two groups, changed and unchanged. Firstly, the proposed method incorporates the local information in order to restrain the impact of speckle noise. Secondly, the proposed method simulates the immune response process in a fuzzy way to get an accurate result by retaining more image details. We introduce a fuzzy membership of the antigen and then update the antibodies and memory cells according to the membership. Compared with the clustering algorithms we have proposed in our previous works, the new method inherits immunological properties from immune systems and is robust to speckle noise due to the use of local information as well as fuzzy strategy. Experiments on real synthetic aperture radar images show that the proposed method performs well on several kinds of difference images and engenders more robust result than the other compared methods.     

基于扩展分形和CFAR特征融合的SAR图像目标识别

研究了多信息融合技术在SAR图像目标识别中的应用。将扩展分形特征(Extended Fractal)与双参数恒虚警特征(Double Parameter CFAR)形成的多信息进行融合处理。运用Dempster-Shafer证据理论，在决策层对SAR图像中的像素进行识别分类。实验结果表明通过融合对像素分类的准确性明显好于单特征的检测结果，减少了虚警概率，提高了系统的识别能力。     

高分辨率合成孔径雷达图像舰船检测方法

寻找针对高分辨率SAR图像的舰船目标检测算法。利用KSW双阈值分割技术，其效果比传统检测方法好，有利于进一步的目标分类和识别。且必须根据SAR图像分辨率来选择舰船检测算法。     

电磁波在包含各向异性媒质多层介质中传播的分析

对电磁波在包含各向异性媒质多层介质中的传播进行分析，给出了闭合形式的解，该结构可用作法拉第旋转器，与其它准光元件组合构成的准光环行器或隔离器。也可用作辐射口径，通过改变磁化场方向和强度实现波束扫描或极化变化，给出了法拉第旋转角计算结果和实验结果。     

拉盖尔-高斯光束通过有硬边光阑光学系统的传输

采用将硬边光阑展为有限个复高斯函数之和的方法,得到了拉盖尔-高斯光束通过有硬边光阑近轴ABCD光学系统中传输的近似解析公式,作了数值计算以说明传输公式的应用,并对使用该式的误差作了讨论。     

一种快速的海面特征SAR成像仿真技术

水下物体运动引起扰动，会在海面产生尾迹。这种尾迹特征同运动物体的参数和下潜的深度以及海况有关。研究这种尾迹特征及其合成孔径雷达(SAR)成像，就有可能利用SAR来检测这个水下运动物体。本文给出了一种水下运动物体的伴流尾迹的快速模拟方法及其合成孔径雷达(SAR)成像仿真方法，并对模拟仿真的结果进行了讨论。     

叠前深度偏移中的旅行时并行计算技术

旅行时计算技术是基于波动方程叠前深度偏移方法的核心内容，射线跟踪的数值计算精度和效率直接影响着介质成像质量和实用化。基于三维射线跟踪的微变网络方法和波前重建思路，提出一种适用于三维复杂介质地震波走时的快速算法。但它所需计算量巨大。采用并行计算技术，设计出基于“任务缓冲池”的动态负载平衡策略，在现有的分布式计算机上，实现了三维射线追踪和叠前深度偏移的并行计算。     

稀布阵综合脉冲孔径雷达旁瓣对消性能分析

定量分析了阵列孔径、干扰信号带宽、通道幅相误差以及Ａ／Ｄ变换器的量化噪声对大型稀布圆形阵列天线干扰对消效果的影响，介绍了稀布阵综合脉冲孔径雷达（ＳＩＡＲ）采用抽头延迟线以提高干扰对消性能的方法，并给出了数值计算与计算机模拟结果。