卫星天线反射面板热形变面形误差检测方法

为实现航天卫星天线面板热形变面形误差高精度检测,提出利用三坐标测量系统搭载高精度激光位移传感器,对检测对象进行扫描测量;并通过对其系统误差来源进行分析、公差合理分配及误差控制,提出对测量系统进行整体优化,以满足所提出检测精度要求。为验证所提出方法的可行性,搭建测量系统,对标准试件的测量数据进行面形拟合,获得测量偏差优于±4.22μm/m2;分别制作常规铝板及光滑铝板作为测试对象,在其背部黏贴加热板的加热方式,以模拟卫星面板在实际工作环境中发生热形变。采用提出的方法对其进行了热形变面形误差检测实验,检测结果验证了所提出方法的可行性。     

基于动态时间阈值的报警聚合方法研究

针对网络入侵检测中持续性攻击引发的多个报警事件时间间隔变化的问题,引入时间间隔变异系数描述报警的时间波动特征;通过将报警数据属性分为时间约束属性和相似度约束属性,提出了一种利用动态时间阈值约束的相似报警数据聚合方法。实验结果表明,这种方法能有效减少持续性攻击触发的冗余报警。     

Real-time fault detection method based on belief rule base for aircraft navigation system

Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base (BRB), where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.     

一种模糊飞行冲突实时检测方法研究

为了解决飞行冲突检测中虚警和漏警问题，提出飞机之间的模糊空间接近度概念以及一种模糊冲突检测方法。该方法通过实时计算飞机对之间垂直接近度与水平接近度，并使用模糊推理规则来判断飞机当前相对接近趋势以及是否存在冲突。仿真实验表明：该冲突检测方法能有效减少飞行冲突的虚警和漏警。     

Quantum dosimetry and online visualization of X-ray and charged particle radiation in commercial aircraft at operational flight altitudes with the pixel detector Timepix

We investigate the application of the hybrid semiconductor pixel detector Timepix for precise characterization, quantum sensitivity dosimetry and visualization of the charged particle radiation and X-ray field inside commercial aircraft at operational flight altitudes. The quantum counting capability and granularity of Timepix provides the composition and spectral-characteristics of the X-ray and charged-particle field with high sensitivity, wide dynamic range, high spatial resolution and particle type resolving power. For energetic charged particles the direction of trajectory and linear energy transfer can be measured. The detector is operated by the integrated readout interface FITPix for power, control and data acquisition together with the software package Pixelman for online visualization and real-time data processing. The compact and portable radiation camera can be deployed remotely being controlled simply by a laptop computer. The device performs continuous monitoring and accurate time-dependent measurements in wide dynamic range of particle fluxes, deposited energy, absorbed dose and equivalent dose rates. Results are presented for in-flight measurements at altitudes up to 12 km in various flights selected in the period 2006–2013.     

Developing a dust storm detection method combining Support Vector Machine and satellite data in typical dust regions of Asia

Enhancing the dust storm detection is a key part for the environmental protection, human healthy and economic development. The goal of this paper is to propose a new Support Vector Machine (SVM)-based method to automatically detect dust storms using remote sensing data. Existing methods dealing with this problem are usually threshold-based that are of great complexity and uncertainty. In this paper we propose a simple and reliable method combining SVM with MODIS L1 data and explore the optimal band combinations used as the feature vectors of SVM. The developed method was evaluated by MODIS and OMI data qualitatively and quantitatively on three study sites located in the Arabian Desert, Gobi Desert and Taklimakan Desert, and it was also compared to three other traditional methods based on their accuracy, complexity, reliability and sensitivity to thresholds. The detection results demonstrated that the combination of (Band7 − Band3)/(Band7 + Band3) ((B7 − B3)/(B7 + B3)), Band20 − Band31 (B20 − B31), and Band31/Band32 (B31/B32) can detect the dust storms more precisely than other individual bands or their combination. The comparison among those cases indicated that the proposed automatic method exhibited an advantage of minimizing the uncertainty and complexity, which were the limits of defining thresholds based on the threshold-based methods. The conclusions can provide references for studies that focus on statistical-based dust storm detection.     

基于增量学习的高光谱图像目标检测

高光谱图像目标检测是高光谱图像分析中的重要研究内容之一。本文从经典有效的约束能量最小化算法出发,提出了一种基于增量学习的高光谱目标检测方法。当获得新的样本时,不需要重新计算所有样本的自相关矩阵即可对检测器模型进行更新,减轻了星上有限计算资源的负担。实验结果表明:本文提出的目标检测算法在压制背景光谱的同时可以更好地适应目标光谱,提高了算法的检测性能。     

Three-line structured light vision system for non-cooperative satellites in proximity operations

Adapter ring is a commonly used component in non-cooperative satellites, which has high strength and is suitable to be recognized and grasped by the space manipulator. During proximity operations, this circle feature may be occluded by the robot arm or limited field of view. Moreover, the captured images may be underexposed when there is not enough illumination. To address these problems, this paper presents a structured light vision system with three line lasers and a monocular camera. The lasers project lines onto the surface of the satellite, and six break points are formed along both sides of the adapter ring. A closed-form solution for real-time pose estimation is given using these break points. Then, a virtual structured light platform is constructed to simulate synthetic images of the target satellite. Compared with the predefined camera parameters and relative positions, the proposed method is demonstrated to be more effective, especially at a close distance. Besides, a physical space verification system is set up to prove the effectiveness and robustness of our method under different light conditions. Experimental results indicate that it is a practical and effective method for the pose measurement of on-orbit tasks.     

A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train

Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter. During health condition monitoring, the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear train. According to actual situation, this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the high-dimensional original feature space. In MCFFSA, a fault feature set of multiple domains, including time domain, frequency domain and wavelet domain, is first extracted from the raw vibration dataset. Four targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets, wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM). Further, F-measure is adopted to identify the optimal feature subset, which was employed for subsequent fault diagnosis. The effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission platform. The experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA, and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.     

面向飞行员疲劳状态监测的脑认知深度模型研究

通过分析飞行员脑电信号，构建了疲劳状态的彩色脑功率图，设计基于高斯牛顿在线变分方法的卷 积神经网络参数优化方法，形成一种新型脑功率图深度网络模型，有效实现脑功率图深度网络的模型分类识别 能力。相比于其它基于脑电信号的疲劳检测深度模型，疲劳状态认知的准确度提升了3%~5%。