低波段天基雷达射频干扰机理及抑制方法

天基合成孔径雷达(SAR)是一种重要的主动遥感设备,在测绘、目标警戒、资源探测等方面具有独特的全天时、全天候优势。但由于低波段电磁频谱异常拥挤复杂,也容易受到其他无线电设备的非蓄意射频干扰(RFI)。本文针对工作于P、L波段的天基SAR系统,分析其常见的地面射频干扰源及其干扰机理,并构建典型的窄带与宽带干扰模型,比较分析了频域陷波、最小均方(LMS)算法、自适应线谱增强(ALE)、特征子空间分解与时频滤波等多种干扰抑制算法,并在卫星实测数据的基础上进行仿真验证抑制宽带干扰的有效性。仿真结果表明:时频滤波算法抑制宽带干扰效果最好,造成的信号损失最小,能够为后续天基合成孔径雷达的抗干扰算法设计提供决策依据。     

雷达图像幅值空间变迹算法

针对旁瓣抑制，给出了一种幅值空间变迹算法。该算法与一般空间变迹法不同，主要通过每一像素点幅值与相邻两个Nyquist采样点幅值之和做比较来判别该点处于主瓣还是旁瓣，来加以不同处理。在整数倍Nyquist采样的情况下，这种算法不仅压低旁瓣还使主瓣变窄，能得到比传统SVA更高的分辨率，在非整数倍Nyquist采样的情况下也比SVA具有更好的稳健性。对此进行了实验研究，结果证实了该算法的有效性。     

用大面积源检定核应急机载/车载伽玛能谱仪

人工核素大面积源标准是核工业航测遥感中心等单位经过十余年研究建立的新型计量保障标准,使用该标准,可对监测核应急伽玛辐射环境的机载/车载伽玛能谱仪进行检定.使用大面积源标准检定,可以有效地弥补因机载/车载伽玛能谱仪系统的角响应影响因素复杂而难于模拟计算的缺陷,为监测核应急伽玛辐射环境的机载/车载伽玛能谱仪提供了量值传递的保障.本文介绍了大面积源检定的原理和技术方法.     

A non-local vectorial total variational model for multichannel SAR image speckle suppression

This paper aims at the multichannel synthetic aperture radar(SAR)image speckle reduction.This paper proposes a novel energy minimized regularization model for multichannel image denoising,which is an extension of the non-local total variational model for gray-scale image.It contains two terms,namely the vectorial data fidelity term and the non-local vectorial total variation term.The latter is constructed by high-dimensional non-local gradient that contains the structure information of the multichannel image.The existence and the uniqueness of the solution of the model are proved.A fixed point iterative algorithm is designed to acquire the solution of this model.The convergence property of this algorithm is proved as well.This model is applied to the multipolarimetric and multi-temporal RADARSAT-2 images despeckling.The result shows that this model performs better than the original vectorial total variational model on texture preserving.     

星载SAR在轨成像实时处理算法研究

星载合成孔径雷达(SAR)分辨率和幅宽的不断提高,其产生的原始数据率越来越大,采用在轨实时成像处理形成图像,在图像域再进行传统的图像数据处理和传输,有助于降低星-地数据传输速率,为了提高星载SAR在轨成像处理的实时性,文章分析了星上特殊条件对星载SAR处理算法实时性所提出的约束,对RD、波束域、CS等经典的星载SAR成像处理算法的运算量进行了比较,以CS算法为例,研究了相位补偿因子对CS成像处理算法的实时性影响,提出了一种补偿因子区域不变的CS成像算法,它极大地减少了成像处理算法的运算量,并通过仿真实验结果验证了该算法的有效性。     

An extended chirp scaling algorithm for spaceborne sliding spotlight synthetic aperture radar imaging

A system impulse response with low sidelobes is critical in synthetic aperture radar（SAR） images because sidelobes contribute to noise and interfere with nearby scatterers. However,the conventional tricks of sidelobe suppression are unable to be exactly applied to the case of spaceborne sliding spotlight SAR due to great azimuth shifts in both time and frequency domains. In this paper, an extended chirp scaling algorithm is presented for spaceborne sliding spotlight SAR data imaging. The proposed algorithm firstly uses the spectral analysis（SPECAN） technique to avoid the azimuth spectrum folding effect and then employs the chirp scaling（CS） algorithm to achieve data focusing, i.e., the so-called two-step approach. To suppress the sidelobe level, an efficient strategy for the azimuth spectral weighting which only involves matrix multiplications and short fast Fourier transformations（FFTs） is proposed, which is a post-process executed on the focused SAR image and particularly simple to be implemented. The SAR image processed by the proposed extended CS algorithm is very precise and perfectly phase-preserving. In the end, computer simulation results verify the analysis and confirm the validity of the proposed algorithm.     

民用飞机机载软件计划阶段评审方法研究

民用飞机机载软件的合格审定是民用飞机适航取证工作的重要内容,机载软件计划阶段评审是机载软件合格审定过程的重要里程碑,直接影响民用飞机主制造商的软件研制进度和适航取证进度。本文结合我国民用飞机机载软件研制过程中计划阶段工程评审与适航审查的经验,总结了适合我国国情的机载软件计划阶段评审的方法和流程,并对此过程中有关的重要问题进行了论述。     

基于管道-过滤器模式的机载导航数据库格式化技术研究

针对飞行管理系统机载导航数据库应用需求,以管道-过滤器模式的软件架构体系为基础,设计了机载导航数据库格式化软件,实现标准导航数据格式到机载导航数据库格式的转换,生成适用于飞行管理系统的机载导航数据库文件系统。在分析机载导航数据库格式化的需求上,采用一种改进的管道-过滤器模式,通过裁剪和筛选导航数据的方法,降低数据容量。利用Access数据库作为中间数据库,建立不同类别的数据表,实现数据的快速查询和对比。通过实际的验证和应用表明机载导航数据库格式化软件是可行的,生成的机载导航数据库文件系统符合飞行管理系统要求,格式化后的数据是正确的。     

SAR图像匹配制导精度影响因素分析

合成孔径雷达（SAR）具有高分辨率、全天候等优点,是提高精确制导武器制导精度的有力工具。制导精度是精确制导武器的重要性能指标,影响SAR制导系统精度的主要因素有实时图的获取误差及预处理误差、图像匹配误差、惯导测量误差、SAR制导特殊弹道引起的导航制导的误差。概述了SAR图像匹配制导系统精度的相关研究,对比和分析了这些研究和结论,指出了该研究存在的问题和发展方向。     

Advanced fractal approach for unsupervised classification of SAR images

Unsupervised classification of Synthetic Aperture Radar (SAR) images is the alternative approach when no or minimum apriori information about the image is available. Therefore, an attempt has been made to develop an unsupervised classification scheme for SAR images based on textural information in present paper. For extraction of textural features two properties are used viz. fractal dimension D and Moran’s I. Using these indices an algorithm is proposed for contextual classification of SAR images. The novelty of the algorithm is that it implements the textural information available in SAR image with the help of two texture measures viz. D and I. For estimation of D, the Two Dimensional Variation Method (2DVM) has been revised and implemented whose performance is compared with another method, i.e., Triangular Prism Surface Area Method (TPSAM). It is also necessary to check the classification accuracy for various window sizes and optimize the window size for best classification. This exercise has been carried out to know the effect of window size on classification accuracy. The algorithm is applied on four SAR images of Hardwar region, India and classification accuracy has been computed. A comparison of the proposed algorithm using both fractal dimension estimation methods with the K-Means algorithm is discussed. The maximum overall classification accuracy with K-Means comes to be 53.26% whereas overall classification accuracy with proposed algorithm is 66.16% for TPSAM and 61.26% for 2DVM.