Automatic spectral target recognition in hyperspectral imagery

Automatic target recognition (ATR) in hyperspectral imagery is a challenging problem due to recent advances of remote sensing instruments which have significantly improved sensor's spectral resolution. As a result, small and subtle targets can be uncovered and extracted from image scenes, which may not be identified by prior knowledge. In particular, when target size is smaller than pixel resolution, target recognition must be carried out at subpixel level. Under such circumstance, traditional spatial-based image processing techniques are generally not applicable and may not perform well if they are applied. The work presented here investigates this issue and develops spectral-based algorithms for automatic spectral target recognition (ASTR) in hyperspectral imagery with no required a priori knowledge, specifically, in reconnaissance and surveillance applications. The proposed ASTR consists of two stage processes, automatic target generation process (ATGP) followed by target classification process (TCP). The ATGP generates a set of targets from image data in an unsupervised manner which will subsequently be classified by the TCP. Depending upon how an initial target is selected in ATGP, two versions of the ASTR can be implemented, referred to as desired target detection and classification algorithm (DTDCA) and automatic target detection and classification algorithm (ATDCA). The former can be used to search for a specific target in unknown scenes while the latter can be used to detect anomalies in blind environments. In order to evaluate their performance, a comparative and quantitative study using real hyperspectral images is conducted for analysis.     

航拍高光谱图像中基于投影的自动目标检测算法

针对未知环境条件下的高光谱图像目标检测问题进行了研究，提出了一种基于投影的自动目标检测算法。该算法通过构造正交投影算子预先对部分干扰物信息进行削弱，再以无监督的自动目标搜寻方法找到场景中可能的目标物，将图像数据向可能目标物所张成的子空间投影以增强目标物的信息，然后用匹配的方法完成检测。有效减弱了干扰物对目标检测的影响，缩小了目标搜索的范围。应用此算法对实验采集数据进行处理，取得了较好的结果。     

基于深度学习的航空器异常飞行状态识别

飞行设备快速存取记录仪(Quick Access Recorder,以下简称QAR)保留了原始航班各类重要飞行参数在内的航行信息,使研究分析航空器实时状况和保障飞行质量成为可能。针对QAR数据高维大样本的特点,在如今大数据背景下,除了传统机理建模分析航空器飞行状态外,采用深度学习的方式建立基于数据驱动的航空器飞行状态识别模型,理论与实用意义兼具。通过对真实QAR飞行数据的研究,开发了基于深度稀疏受限玻尔兹曼机的异常飞行状态识别程序。首先利用小波降噪技术对原始飞行数据进行预处理清洗,在一系列典型飞行参数上提取经典时域特征以及小波奇异熵等信息熵特征构成特征集。在此基础上,分别利用经典的线性主元分析技术和深度稀疏玻尔兹曼机对特征集进行有效降维,最后采用四折交叉验证方式,通过高斯过程分类器实现对飞行状态的辨识。实验结果显示,基于深度受限玻尔兹曼机-高斯过程分类的飞行状态识别具有较高分类准确性。     

Spectral-spatial target detection based on data field modeling for hyperspectral data

Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed. The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task. Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates (FARs) with respect to those achieved by conventional hyperspectral target detectors.     

基于空间多特征综合推理的航迹航路关联

针对航迹分类问题,研究了基于空间多特征的综合推理在航路判读中的应用。首先根据空管系统对航路以及飞机飞行的要求,对航迹航路相关问题进行建模。然后根据已知的传感器系统输出的目标特性(位置,航向)与已知的多个航路信息分别进行相关度计算,构造基本信任函数,通过对其融合,得到目标单特征识别结果。其中,通过合理地引入复合类,实现了对目标类别的广义信任分类。建立了多特征折扣融合算法,对多特征基本信任函数进行折扣后再融合,得到目标多特征识别结果。仿真结果以及空管实际数据测试表明:该算法不仅可以实现航迹分类,同时可以有效地降低分类的错误率。     

Multiaspect classification of airborne targets via physics-basedHMMs and matching pursuits

Wideband electromagnetic fields scattered from N distinct target-sensor orientations are employed for classification of airborne targets. Each of the scattered waveforms is parsed via physics-based matching pursuits, yielding N feature vectors. The feature vectors are submitted to a hidden Markov model (HMM), each state of which is characterized by a set of target-sensor orientations over which the associated feature vectors are relatively stationary. The N feature vectors extracted from the multiaspect scattering data implicitly sample N states of the target (some states may be sampled more than once), with the state sequence modeled statistically as a Markov process, resulting in an HMM due to the “hidden” or unknown target orientation. In the work presented here, the state-dependent probability of observing a given feature vector is modeled via physics-motivated linear distributions, in lieu of the traditional Gaussian mixtures applied in classical HMMs. Further, we develop a scheme that yields autonomous definitions for the aspect-dependent HMM states. The paradigm is applied to synthetic scattering data for two simple targets     

高超声速飞行器的滑模观测器设计

对一个高超声速飞行器设计了滑动模态观测器来估计攻角和航迹倾角。通过实时的求解一个极点配置问题,使观测器的切换增益能够根据观测器状态的变化而改变,从而保证了观测误差的动态特性在整个飞行包线内的一致性,给出了单输出和多输出非线性系统的滑模观测器设计过程。设计和仿真算例验证了这种方法的有效性。     

基于多尺度 3D-2D卷积神经网络的高光谱图像分类

设计提出了 1种针对高光谱图像分类任务的 3D-MSCNN模型。在 PCA降维的基础上,利用 3D空谱特征提 取网络和 2D多尺度特征提取网络实现高光谱图像特征提取,充分发挥高光谱图像空谱信息价值,增强对不同尺度 地表覆盖的表达能力。最后,利用 Softmax分类损失函数实现高光谱图像分类任务。实验结果表明,本文算法在 In. dian Pines和 Pavia University数据集上都取得了较好的分类效果。与 CD-CNN、3D-CNN、SS-Net和 HybirdSN等方法相 比,本文算法能够有效提升总体精度、平均精度和 Kappa系数等客观评价指标。     

特殊机场PBN运行的扰动风场估计及关联分析

高原特殊机场的民航PBN运行受到地形复杂、导航干扰、程序设计缺陷及大气扰动等多重复杂因素的影响.大气扰动是导致PBN运行的飞行技术误差乃至飞行事故的重要诱因.分析了扰动风对飞行的影响机理,建立了气流系下含扰动风动力学模型.基于QAR数据的解析冗余,计算获得实时扰动风场.在此基础上,运用灰色关联分析方法,分析扰动风对若干飞行状态的影响.灰色关联分析结果既是对扰动风影响机理的验证,也可进一步用于QAR数据分析,从而深入研究扰动风对PBN运行的飞行技术误差的影响.     

高超声速飞机动力需求探讨

近年来,随着高超声速技术的长足进步,特别是在超燃冲压技术逐渐面向工程化的背景下,关于高超声速飞机及其动力系统的讨论也频繁出现。为了在宽速域条件下工作,基于不同热力循环工作模式的组合动力系统相继被提出,高超声速飞机的动力发展形式出现了百花齐放、百家争鸣的局面,也对高超声速飞机动力系统的选型提出了巨大挑战。通过对飞机发展史及高超声速相关发展技术的综述,阐述了现阶段组合动力是高超声速飞机动力主要发展方向这一结论,针对高超声速飞机需求,梳理和分析了几种高超声速组合动力系统的工作原理、工作特性及优缺点,并展望了采用组合动力系统对未来高超声速飞机研究带来的挑战。随着飞行速度的提高,高超声速飞机和动力系统的一体化势在必行。     

高超声速飞行器表面测热技术综述

高超声速飞行中飞行器表面气动加热量是飞行器热防护系统最为关键的设计输入,在理论计算与地面模拟的有限近似条件下,通过飞行试验实时获取真实环境下飞行器表面的气动加热量,并在此基础上完成对计算模型与地面试验的验证与改进具有重要意义。详细列举了20世纪50年代以来,国外具有代表性的飞行试验与测热方案。以“内置式”与“嵌入式”作为测热技术的分类特征,介绍了各类测试设备及相应的飞行试验结果。着重分析了“热匹配性”与“结构匹配性”作为关键因素对飞行测热技术的影响,通过飞行试验实例介绍了该问题的解决方法及工程经验。归纳了飞行测热技术发展的共性、特点与未来趋势,并结合当前我国发展现状,对该领域未来研究提出建议。     

临近空间飞行器推进系统预冷器关键技术

临近空间由于其在现代战争中的重要战略意义已成为各国航空航天领域的研究重点,高超声速飞行器更是国家临近空间军事实力的一个重要标志。由于吸气式高超声速飞行器具有较高的飞行高度与马赫数,预冷技术已成为高超声速飞行器推进系统中的一项关键技术,而高性能预冷器设计是预冷技术的一个重要研究方向,预冷器的可靠性与流动传热特性是预冷系统的重要影响参数,对于紧凑、高效、高可靠性先进预冷器的研究具有十分重要的意义。基于目前公开的临近空间高超声速飞行器的主要动力形式及其对预冷技术的刚性需求,对预冷器设计中的关键技术与发展方向做了详细的阐述。     

面向控制的高超声速飞行器总体优化设计方法

从前期高超声速飞行器基础理论以及关键技术的研究到现今的飞行试验可以看出,高超声速飞行器在总体设计阶段过晚考虑飞行器动力学特性和控制性能会给后期控制系统设计带来极大的困难。在对两类高超声速飞行器试飞与控制相关问题分析的基础上,提出了面向控制的高超声速飞行器总体优化设计方法,给出了面向控制的总体优化设计研究方法、思路和模块框架。     

Active Aerothermoelastic Control of Hypersonic Double-wedge Lifting Surface

Designing re-entry space vehicles and high-speed aircraft requires special attention to the nonlinear thermoelastic and aerodynamic instability of their structural components. The thermal effects are important since temperature environment brings dramatic influences on the static and dynamic behaviors of flight structures in supersonic/hypersonic regimes and is likely to cause instability, catastrophic failure and oscillations resulting in structural failure due to fatigue. In order to understand the dynamic behaviors of these ＂hot＂ structures, a double-wedge lifting surface with combining freeplay and cubic structural nonlinearities in both plunging and pitching degrees-of-freedom operating in supersonic/hypersonic flight speed regimes has been analyzed. A third order piston theory aerodynamic is used to estimate the applied nonlinear unsteady aerodynamic loads. Also considered is the loss of torsional stiffness that may be incurred by lifting surfaces subject to axial stresses induced by aerodynamic heating. The aerodynamic heating effects are estimated based on the adiabatic wall temperature due to high speed airstreams. As a recently emerging technology, the active aerothermoelastic control is aimed at providing solutions to a large number of problems involving the aeronautical/aerospace flight vehicle structures. To prevent such damaging phenomena from occurring, an application of linear and nonlinear active control methods on both flutter boundary and post-flutter behavior has been fulfilled. In this paper, modeling issues as well as numerical simulation have been presented and pertinent conclusions outlined. It is evidenced that a serious loss of torsional stiffness may induce the dynamic instability; however active control can be used to expand the flutter boundary and convert unstable limit cycle oscillations （LCO） into the stable LCO and/or to shift the transition between these two states toward higher flight Mach numbers.     

Supervised learning with probability interpretation in airfoil transition judgment

Transition prediction has always been a frontier issue in the field of aerodynamics. A supervised learning model with probability interpretation for transition judgment based on experimental data was developed in this paper. It solved the shortcomings of the point detection method in the experiment, that which was often only one transition point could be obtained, and comparison of multi-point data was necessary. First, the Variable-Interval Time Average (VITA) method was used to transform the fluctuating pressure signal measured on the airfoil surface into a sequence of states which was described by Markov chain model. Second, a feature vector consisting of one-step transition matrix and its stationary distribution was extracted. Then, the Hidden Markov Model (HMM) was used to pre-classify the feature vectors marked using the traditional Root Mean Square (RMS) criteria. Finally, a classification model with probability interpretation was established, and the cross-validation method was used for model validation. The research results show that the developed model is effective and reliable, and it has strong Reynolds number generalization ability. The developed model was theoretically analyzed in depth, and the effect of parameters on the model was studied in detail. Compared with the traditional RMS criterion, a reasonable transition zone can be obtained using the developed classification model. In addition, the developed model does not require comparison of multi-point data. The developed supervised learning model provides new ideas for the transition detection in flight experiments and other experiments.     

Criteria for hypersonic airbreathing propulsion and its experimental verification

Hypersonic airbreathing propulsion is one of the top techniques for future aerospace flight, but there are still no practical engines after seventy years' development. Two critical issues are identified to be the barriers for the ramjet-based engine that has been taken as the most potential concept of the hypersonic propulsion for decades. One issue is the upstream-traveling shock wave that develops from spontaneous waves resulting from continuous heat releases in combustors and can induce unsteady combustion that may lead to engine surging during scramjet engine operation. The other is the scramjet combustion mode that cannot satisfy thrust needs of hypersonic vehicles since its thermos-efficiency decreases as the flight Mach number increases. The two criteria are proposed for the ramjet-based hypersonic propulsion to identify combustion modes and avoid thermal choking. A standing oblique detonation ramjet (Sodramjet) engine concept is proposed based on the criteria by replacing diffusive combustion with an oblique detonation that is a unique pressure-gain phenomenon in nature. The Sodramjet engine model is developed with several flow control techniques, and tested successfully with the hypersonic flight-duplicated shock tunnel. The experimental data show that the Sodramjet engine model works steadily, and an oblique detonation can be made stationary in the engine combustor and is controllable. This research demonstrates the Sodramjet engine is a promising concept and can be operated stably with high thermal efficiency at hypersonic flow conditions.     

增程飞行器高超声速飞行的动态特性

研究了增程飞行器高超声速飞行时的动态特性.短程导弹和火箭弹等高超声速飞行来源于增程的实际需要,从超声速到高超声速飞行所遇到的主要飞行动力学问题是,尽管在增程前低超声速时具有良好稳定性,但在高超声速飞行中会丧失稳定性并导致复杂的锥动.通过飞行和风洞实验表明:高超声速飞行时增程飞行器固有的横侧向稳定特性丧失,带有弱滚转阻尼的不稳定荷兰滚振荡将导致滚转-偏航耦合,此外还存在由耦合导致的轻微不稳定拟周期模态和滚动共振.     

推力耦合的高超声速飞行器气动伺服弹性研究

对于采用吸气式超燃冲压发动机的高超声速飞行器,其发动机推力可能与机身弹性发生耦合影响,从而引起所谓的推力耦合气动伺服弹性(ASE)问题。为对其耦合原理及影响进行研究,以简化的飞行器纵向模型为对象,考虑结构弹性、非定常气动力、冲压发动机以及控制系统之间的相互耦合作用,建立了推力耦合的高超声速飞行器气动伺服弹性问题的一般建模框架和分析流程。采用牛顿冲击理论计算高超声速非定常气动力,基于准一维流动假设分析发动机性能。算例结果表明,考虑发动机推力的耦合影响后,飞行器的短周期特性和气动伺服弹性特性均有明显改变,气动伺服弹性稳定裕度下降可达16%,应当引起飞行控制系统设计部门的重视。     

高超声速飞行器组合导航算法

以高超声速飞行器为对象,建立了卫星/惯性组合导航模型。选择组合系统的状态量并建立状态方程,推导位置观测方程和速度观测方程,通过位置、速度交替组合实现卫星/惯性系统的组合。通过仿真验证算法的有效性,仿真结果表明卫星/惯性组合导航可以得到较好的位置和速度信息,适中的姿态精度信息。     

航空发动机传感器解析余度模型的建立方法

为了使用解析余度模型对传感器故障进行诊断,提出了1种基于K-均值聚类与改进微分进化算法优化的极端学习机(IDE-ELM)的发动机传感器解析余度模型建立方法。为避免求解ELM算法时H矩阵奇异,采用K-均值聚类对试验数据进行聚类处理,然后从每类数据中选取1组数据组成训练样本用于训练;利用IDE算法优化ELM的输入层权值和偏置,提高ELM的泛化能力。利用飞行试验数据进行了仿真验证。结果表明:基于K-均值聚类和IDE-ELM设计的传感器解析余度模型具有较高的精度,可用于FADEC系统双通道传感器的故障诊断。