一种斜视滑动聚束SAR子孔径处理成像方法

子孔径法是解决滑动聚束SAR成像方位频谱混叠的有效方法,具有内存占用量低、灵活性高的特点。子孔径的划分不仅影响成像 质量,还会影响成像效率;但目前子孔径的划分大多采用经验值,理论上并没有给出明确分析。因此在斜视成像过程中,未合理划分的子孔径会导致拼接谱出现“空隙”现象。本文首先介绍了斜视滑动聚束SAR工作模型,分析了其方位多普勒历程,提出了斜视下基于方位频域Scaling的子孔径成像算法。根据相邻子孔径的二维频谱,研究了子孔径频谱拼接的准则,推导了确定子孔径长度与重叠率的解析式,给出了子孔径划分的流程,最后通过仿真 实验和实测数据处理验证了子孔径划分方法及斜视下成像算法的有效性。     

大斜视双基地SAR的二维可分离成像算法

提出了大斜视双基地SAR的二维可分离成像算法.首先建立了大斜视双基地SAR回波信号模型,并对其距离徙动和方位调频率的变化特性进行了分析;在此基础上,将二维可分离成像算法推广应用到了双基地SAR领域,解决了大斜视双基地SAR回波信号线性距离徙动补偿后方位调频率在方位向出现的空变性问题.算法采用了由平移不变双基地SAR几何关系所确定的新匹配函数,并考虑了3次相位项对成像效果的影响,改善了成像的效果.最后通过点目标仿真验证了算法的有效性.     

非平行航线双基聚束SAR成像

文中通过对非平行航线双基SAR信号模型的分析,利用瞬时频率和瞬时斜距的概念,提导了非平行双基SAR二维波数域表达式.利用级数反演给出了基于平面波假设的非平行双基SAR分离的发收Doppler表达式.然后引入最短斜距和和最短斜距差的概念,提出了频率变标非平行航线双基聚束SAR的成像算法.最后通过仿真数据验证了算法的有效性.     

双站聚束SAR极坐标格式成像算法

双站聚束SAR(Synthetic aperture radar)相对于双站条带SAR可以达到更高的方位分辨率,其成像算法受到越来越多的关注.本文介绍了双站聚束SAR成像几何关系,建立了回波信号模型,从Dechirp后的回波数据域出发,提出了一种适合双站聚束SAR的极坐标格式成像算法.该PFA算法采用斜视坐标旋转及沿视线方向的极坐标插值LOSPI,更好地利用空间域数据,适用于正侧视和斜视模式成像.最后分析了距离弯曲对成像区域的限制,以及残余视频相位误差对成像性能的影响,并将此算法与不进行Dechirp的PFA算法相比较.实验结果表明该方法降低了数据率和运算量,算法有效、可靠.     

一种改进的前斜视SAR距离多普勒成像算法

根据大斜视角SAR成像的空间几何模型和回波信号特点,提出了一种改进的适用于前斜视场景的距离-多普勒成像算法;对补偿二维频域耦合相位项的条件进行了分析,分析表明只需根据设计参数决定是否补偿到三阶耦合相位即可,且不必补偿更高阶的耦合相位;对距离徙动的校正方法进行了研究,通过在频域直接对距离徙动进行校正可以避免使用传统的插值方法,有效减小了计算量,提高了算法的运算效率,有助于算法的实时性实现;对改进的距离-多普勒算法的仿真表明,其成像性能能够满足前斜视条件下SAR实时成像处理的要求。     

基于数字聚束技术的双基SAR PFA波前弯曲补偿算法

双基合成孔径雷达（Synthetic aperture radar, SAR）极坐标格式算法（Polar format a lgorithm, PFA）是基于平 面波前假设建立的,在聚焦过程中会引入波前弯曲误差,使聚焦图像出现空变几何失真和散 焦现象。因此,实际应用双基PFA时其有效成像场景的大小通常受到一定限制。本文提出了 一种基于数字聚束技术的双基PFA波前弯曲误差补偿新方法。该方法首先利用数字聚束预滤 波处理将原始的宽波束划分成多个对应不同子场景的窄波束,然后依次对窄波束数据进行补 偿和成像,最后再通过子场景拼接恢复全场景图像。理论和仿真结果表明该方法能够有效地 补偿波前弯曲误差,扩大双基PFA的有效场景聚焦范围。     

基于多空间多层次谱聚类的非监督SAR图像分割算法

提出了一种基于多层区域谱聚类的非监督SAR图像分割算法（multi-space and multi-hierarchical region based spectral clustering, MSMHSC）。该算法首先在特征与几何空间求距离, 快速获得初始过分割区域, 然后在过分割区域的谱空间上进行聚类, 最终实现非监督的SAR图像分割。该方法计算复杂度小, 无须训练样本, 使用层次化思想使其能更充分地利用SAR图像各类先验与似然信息。在MSTAR真实SAR数据集上的实验验证了该算法的快速性和有效性。     

基于仿真数据的HJ-1C卫星SAR成像处理方法

合成孔径雷达(SAR,synthetic aperture radar)是对地遥感观测的重要技术手段.作为民用星载SAR的第一颗卫星,HJ-1C卫星即将发射,因此文中针对其成像处理中涉及的关键技术开展了研究工作,包括精确的斜视等效距离模型下的扩展ECS(extended chirp scaling)算法的补偿因子、三次相位误差补偿因子形式的分析,快速的回波数据模拟算法的建模和实现,以及精确的Doppler参数计算方法以及对扫描模式成像算法中拼接环节的算法改进.通过理论研究分析和仿真验证,证明基于等效斜视模型的扩展chirp scaling算法是适应条带、扫描成像模式的精确算法.     

基于FK能量聚束的传感器阵列优化布设方法

针对地下浅层震源远场震动信号拾取的需求，开展了震动传感器阵列分布式优化布设方法研究。首先提出了一种基于频率-波数（F-K）分析的能量聚束算法，拾取了远场微弱的震动信号。其次，分析了能量聚束效果的评价指标；最后，利用MATLAB进行了仿真验证，得出了不同步站方式下模拟震源对应的功率谱，并对比分析了它们的聚束效果。经仿真结果表明，在远程震动信号拾取过程中，正六边形阵列这种布站方式聚束效果最好。     

A novel spaceborne SAR wide-swath imaging approach based on Poisson disk-like nonuniform sampling and compressive sensing

Since the range swath width in the conventional single channel spaceborne synthetic aperture radar(SAR)is restricted by the system parameters,there is a trade-off between the azimuth resolution and the swath width in order to satisfy the Nyquist sampling criterion.In this paper,we propose a novel spaceborne SAR wide-swath imaging scheme based on compressive sensing(CS)for the sparse scene.The proposed method designs a Poisson disk-like nonuniform sampling pattern in the azimuth direction,which meets the demand of wider swath by restricting the smallest time interval between any two azimuth samples,with the conventional sampling pattern preserved in the range direction.By a similar way to the processing procedure of spectral analysis(SPECAN)algorithm,the linear range migration correction(RMC)is realized while carrying out range compression,which can meet the demand for focusing with middle level resolution.To reduce the computation load of CS reconstruction,we propose a novel fast reconstruction algorithm based on nonuniform fast Fourier transform(NUFFT),which greatly reduces the computation complexity from O(2M N)to O(4N log N).Experiment results validate the effectiveness of the proposed methods via the point target simulation and the Radarsat-1 raw data processing in F2 mode.     

A novel approach to polarimetric SAR data processing based on Nonlinear PCA

In remotely sensed Synthetic Aperture Radar (SAR) images, scattering from a target is often the result of a mixture of different mechanisms. For this reason, detection of targets and classification of SAR images may be very difficult and very different from other sensor imagery. Fully polarimetric data offer the possibility to separate the different mechanisms, interpret them and consequently identify the geometry of the targets. To achieve this task, several target decomposition techniques have been proposed in the literature to improve the interpretation of this kind of data. Among these, the physical based techniques are the most considered.     

A novel ship classification approach for high resolution SAR images based on the BDA-KELM classification model

Ship classification based on synthetic aperture radar (SAR) images is a crucial component in maritime surveillance. In this article, the feature selection and the classifier design, as two key essential factors for traditional ship classification, are jointed together, and a novel ship classification model combining kernel extreme learning machine (KELM) and dragonfly algorithm in binary space (BDA), named BDA-KELM, is proposed which conducts the automatic feature selection and searches for optimal parameter sets (including the kernel parameter and the penalty factor) for classifier at the same time. Finally, a series of ship classification experiments are carried out based on high resolution TerraSAR-X SAR imagery. Other four widely used classification models, namely k-Nearest Neighbour (k-NN), Bayes, Back Propagation neural network (BP neural network), Support Vector Machine (SVM), are also tested on the same dataset. The experimental results shows that the proposed model can achieve a better classification performance than these four widely used models with an classification accuracy as high as 97% and encouraging results of other three multi-class classification evaluation metrics.     

A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition

Recently, in a task of face recognition, some researchers presented that independent component analysis (ICA) Architecture I involves a vertically centered principal component analysis (PCA) process (PCA I) and ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). They also concluded that the performance of ICA strongly depends on its involved PCA process. This means that the computationally expensive ICA projection is unnecessary for further process and involved PCA process of ICA, whether PCA I or II, can be used directly for face recognition. But these approaches only consider the global information of face images. Some local information may be ignored. Therefore, in this paper, the sub-pattern technique was combined with PCA I and PCA II, respectively, for face recognition. In other words, two new different sub-pattern based whitened PCA approaches (which are called Sp-PCA I and Sp-PCA II, respectively) were performed and compared with PCA I, PCA II, PCA, and sub-pattern based PCA (SpPCA). Then, we find that sub-pattern technique is useful to PCA I but not to PCA II and PCA. Simultaneously, we also discussed what causes this result in this paper. At last, by simultaneously considering global and local information of face images, we developed a novel hybrid approach which combines PCA II and Sp-PCA I for face recognition. The experimental results reveal that the proposed novel hybrid approach has better recognition performance than that obtained using other traditional methods.     

A novel approach for edge detection based on the theory of universal gravity

This paper presents a new, simple and effective low-level processing edge detection algorithm based on the law of universal gravity. The algorithm assumes that each image pixel is a celestial body with a mass represented by its grayscale intensity. Accordingly, each celestial body exerts forces onto its neighboring pixels and in return receives forces from the neighboring pixels. These forces can be calculated by the law of universal gravity. The vector sums of all gravitational forces along, respectively, the horizontal and the vertical directions are used to compute the magnitude and the direction of signal variations. Edges are characterized by high magnitude of gravitational forces along a particular direction and can therefore be detected. The proposed algorithm was tested and compared with conventional methods such as Sobel, LOG, and Canny using several standard images, with and without the contamination of Gaussian white noise and salt & pepper noise. Results show that the proposed edge detector is more robust under noisy conditions. Furthermore, the edge detector can be tuned to work at any desired scale.     

基于旋转屏的一种裸眼立体显示新方法

提出一种基于旋转屏的裸眼立体显示新方法,利用人眼的视觉暂留及旋转过程中双目可视的屏幕区域不同的原理,构成视差。给出了理论分析及部分实验结果,并指出本方法的一些问题和未来研究方向。     

基于FCM颜色聚类的车牌定位方法

提出了一种基于FCM颜色聚类的车牌定位方法。首先应用高斯差分算子对图像进行二值化;其次进行中值滤波;然后利用形态滤波,基于车牌的结构特征进行车牌的粗定位;最后基于FCM颜色聚类进行车牌的精定位。对各种条件下采集的250幅车辆图像进行实验,定位率在98%以上,同时该算法对光照影响有很好的鲁棒性。     

A novel communication system approach for a Smart City based on the human nervous system

The massive introduction of internet, architect of the Internet of Thingsparadigm, was expected to facilitate the spread of people out of the urban spaces. However, the reality is that increasingly people are moving to cities, which generates many challenges for the cities of tomorrow. In this context, the Smart Cityconcept emerges, where Information and Communication Technologies have a key role. This situation has revealed an important issue that needs to be addressed: the lack of an ubiquitous communication architecture able to deal with the expected requirements of a SC. To this end, a wide review of the existing communication frameworks and city services have been performed, which showed that existing proposals are classical fixed ad-hocsolutions for very specific problems with no resilience and common thread. By rethinking the nature of cities and considering them as living organisms, it is possible to relate the nervous systems with the communication architecture of a city. Therefore, this work proposes a novel communication architecture, ubiquitous and resilient, inspired in the human nervous system by the definition of Smart Gateways, able to satisfy the needs of a real SCand adaptable to the growing and specific requirements of every single city. Additionally, the research also includes the simulation of the communication channel with different technologies.