Synergetic use of SAR and Thermal Infrared data to study the physical properties of the lunar surface

The surface layer of the Moon preserves vital evidences of lunar impact and cratering processes due to the absence of any Aeolian and fluvial erosion processes acting on it. By examining these evidences, which are recorded throughout the evolutionary history of the Moon, several basic aspects of lunar science can be understood, and this has direct relevance to the surfaces of other airless bodies within the solar system. In this study, rock abundance data obtained from Thermal Infrared (TIR) observations and radar Circular Polarization Ratio (CPR) data sets obtained from polarimetric SAR observations were correlated at some sample sites on the lunar surface. Preliminary results yielded qualitative and quantitative estimates for surface rock abundances. Except at distal ejecta deposits of young, bright craters a general correlation was observed between the two datasets. Mixed results were observed from the impact melt flows where the situation is complex due to the possible subsurface-volume and volume-subsurface interactions of the radar waves. But the flow features were clearly separated from the interior and ejecta regions of their parent craters in terms of CPR and rock abundances. The extent and distributions of pyroclastic deposits and dark haloed regions could not be distinctly identified at the resolution of datasets utilized. Near Gerasimovich D crater, the Diviner Radiometer has provided the first TIR observations of a newly discovered impact melt flow which was not visible in the optical imagery. This facilitated the first ever quantitative comparisons of the radar CPR and rock abundance values near such a region. Also, significant differences in spatial patterns between the radar and rock concentration data sets were observed, owing to the differences in the sensitivity of the two observations.     

Few-shot incremental radar target recognition framework based on scattering-topology properties

The continuous emergence of new targets in open scenarios leads to a substantial decrease in the performance of Inverse Synthetic Aperture Radar(ISAR)recognition systems.Also,data scarcity further exacerbates the challenge of identifying new classes of ISAR targets.In this paper,a few-shot incremental target recognition framework based on Scattering-Topology Proper-ties(STPIL)is proposed.Specifically,STPIL extracts scattering-topology properties of ISAR tar-gets as recognition features.Meanwhile,the pseudo-incremental training strategy effectively alleviates the algorithm's forgetting of old knowledge,and improves compatibility with new classes.Besides,a feature embedding network,with few parameters,is designed based on the graph neural network.This embedding network is highly adaptable to changes in data distribution.Additionally,STPIL fully considers the joint distribution and marginal distribution in scattering features,and uses the Brownian distance metric module to make the scattering-topology features more discrim-inative.Experimental results on both the simulation dataset and the public measured data indicate that STPIL can effectively balance new classes with old classes,and has superior performance to other advanced methods in the incremental recognition of targets.     

基于双/多基SAR的集群协同探测与制导新进展

在高动态、强对抗、资源受限等复杂环境下;集群作战系统协同探测与制导技术面临严峻挑战。基于双/多基合成孔径雷达（SAR）的协同探测与制导技术;能够实现被动前视高分宽幅成像、多视角信息融合增强以及多方向协同打击;是解决未来复杂环境下集群高可靠协同探测与制导难题、完成感知-认知-决策-执行（OODA）回路闭环的一种有效途径。近年来;随着双/多基SAR技术的进步;双/多基SAR在协同探测与制导领域的研究有了显著发展。旨在对双/多基SAR在集群协同探测与制导中的研究进展进行综述。具体来说;首先探讨基于双/多基SAR的协同探测与制导机理和典型工作模式;同时梳理复杂环境下基于双/多基SAR的协同探测与制导2个核心关键任务的技术需求;然后;介绍复杂环境下基于双/多基SAR的协同探测与制导涉及的各项关键技术及其进展;并对双/多基SAR架构下的协同探测与制导技术瓶颈与挑战展开分析;最后;围绕智能化、抗干扰等多方面挑战;展望基于双/多基SAR的集群协同探测与制导技术未来发展趋势。     

对合成孔径雷达的步进移频干扰

为了使压制干扰获得更多的SAR匹配处理增益,从而进一步降低干扰功率,提出了一种脉内脉间二维部分相干干扰：步进移频干扰。该干扰在保留信号脉内脉间相位特性的同时,让干扰载频在脉间步进递增或递减变化,人为产生距离向和方位向的耦合效应。理论分析和仿真实验表明,该干扰可同时获得SAR在距离向和方位向的部分匹配处理增益,因而降低了干扰功率需求。此外,该干扰的SAR成像输出为一个干扰区域,无需复杂的复合调制即可作为压制干扰掩护分布式目标,干扰实施简单,是一种有效的SAR压制干扰方式。
     

基于ALPFT参数估计的运动目标高分辨率成像

针对调频连续波合成孔径雷达（FMCW SAR）对地面恒加速运动目标的运动参数估计及高分辨率成像问题展开研究。首先;建立FMCW SAR系统下的运动目标回波模型;提出自适应局部多项式傅里叶变换（ALPFT）对高达四阶的多普勒相位进行估计。其次;通过广义Keystone变换和Hough变换对距离徙动项进行校正;同时完成运动目标的速度信息估计。最后;通过补偿高阶多普勒相位以及方位重采样操作;实现对非中心目标的剩余空变相位的补偿;使场景中所有运动目标聚焦效果良好。仿真结果验证了所提方法的可行性和有效性。     

一种基于DPCA的星载SAR运动目标检测方法

文中研究星载条件下用DPCA检测地面慢速运动目标的SAR-GMTI技术,提出一种对杂渡抑制以后的原 始数据应用Radon变换来检测运动目标的新方法。与基于图像域的DPCA运动目标检测方法相比,该方法不需要方位 压缩,因而减小了计算负荷,但没有损失目标检测性能。此外,该方法更加清楚地显示出杂波对目标参数估计的影响,可 为进一步研究提供思路。仿真结果证明这种方法的正确性和有效性。     

改进多尺度Canny算子在SAR图像边缘提取中的应用

着重讨论了在原始Ｃａｎｎｙ算子基础上提出的一种改进算法。其主要特点在于将多尺度分析的概念引入到基本Ｃａｎｎｙ算子中,从而改善了Ｃａｎｎｙ算子的检测性能,使其更加适应合成孔径雷达（ＳＡＲ）图像受噪声影响较大的特点。最后在原始ＳＡＲ图像基础上,将改进的算法与基本Ｃａｎｎｙ算子及其它几类边缘检测算子作了性能对比实验。实验结果显示了改进Ｃａｎｎｙ算子的良好检测性能。     

一种在SAR图像中进行目标识别的多尺度模型

本文基于SAR相干成像的原理,利用当图像分辨率改变时,自然景物和人工目标班点模式变化方式的不同,提出了一种从自然景物中识别人工目标的多尺度模型,并研究了此模型在不同分辨率图像中的性质及应用。     

基于峰值点的目标对准在MSTAR SAR图像分类中的应用

模板匹配的分类方法是合成孔径雷达目标识别的重要方法。研究了MSTAR SAR图像,指出图像中的目标峰值点本质上对应于实际物体的散射中心,并且在一定方位区间中具有稳定性。假设目标存在位置偏移的基础上,提出先利用目标峰值点对准目标,再生成模板和分类的思想。分析了方位区间中基准图像的选取及对准过程对模板生成和目标分类的影响程度。最后研究了同类变形目标间的分类,提出了峰值点特征增强成像、预分类、目标对准和正式分类的四步算法。结果证明,所提算法具有较高的分类精度和效率。     

InSAR成像匹配制导技术

为了解决飞行器远程全天候高精度自主导航的问题,提出一种InSAR成像匹配制导技术,利用InSAR生成的高程数据与基准高程图进行精确匹配,并通过构象模型反演出飞行器的空间位置。分析对比了多种精确地形匹配方法,采用基于F.Leberl数学模型的空间定位方法并分析了误差影响。仿真结果表明,该方法可以实现飞行器高精度自主导航定位,大大提高飞行器的适应性和抗干扰能力。