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。     

D-region ionospheric disturbances associated with the Extremely Severe Cyclone Fani over North Indian Ocean as observed from two tropical VLF stations

The D-region ionospheric disturbances due to the tropical cyclone Fani over the Indian Ocean have been analysed using Very Low Frequency (VLF) radio communication signals from three transmitters (VTX, NWC and JJI) received at two low latitude stations (Kolkata-CUB and Cooch Behar-CHB). The cyclone Fani formed from a depression on 26th April, 2019 over the Bay of Bengal (Northeastern part of the Indian Ocean) and turned into an extremely severe cyclone with maximum 1-min sustained winds of 250 km/h on 2 May, 2019 which made landfall on 3 May, 2019. Out of six propagation paths, five propagation paths, except the JJI-CHB which was far away from the cyclone track, showed strong perturbations beyond $3\sigma$ level compared to unperturbed signals. Consistent good correlations of VLF signal perturbations with the wind speed and cyclone pressure have been seen for both the receiving stations. Computations of radio signal perturbations at CUB and CHB using the Long Wave Propagation Capability (LWPC) code revealed a Gaussian perturbation in the D-region ionosphere. Analysis of atmospheric temperature at different layers from the NASA’s TIMED satellite revealed a cooling effect near the tropopause and warming effects near the stratopause and upper mesosphere regions on 3 May, 2019. This study shows that the cyclone Fani perturbed the whole atmosphere, from troposphere to ionosphere and the VLF waves responded to the disturbances in the conductivity profiles of the lower ionosphere.     

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%。     

基于ADS-B的飞机冲突探测

面对飞行流量持续快速增长、民航空域资源日益短缺的状况,对飞机间的潜在冲突进行有效探测,是避免飞机间危险接近或碰撞发生的关键.对飞机周围区域进行合理建模,给出了一种基于广播式自动相关监视(ADS-B)技术的确定型冲突探测算法,充分利用ADS-B报文提供的经度、纬度、速度和航向等信息及其报文高更新率等特点,预先排除无威胁飞机,然后分别从水平和垂直两个方向对飞机之间的冲突进行探测.运用Matlab进行功能仿真,结果表明该算法能有效排除无威胁的飞机,并且能够对有威胁的飞机及时给出告警.该算法对保证自由飞行条件下的飞行安全具有积极意义.     

空间引力波探测微牛电推进寿命评估研究现状

阐述了空间引力波探测任务中无拖曳控制技术对推进系统的特殊要求，介绍了目前主要的备选推进类型：冷气推进、离子电推进、会切型霍尔电推进、胶体电推进。针对面向空间引力波探测任务的电推进系统的寿命评估工作进行了任务分析，报告了目前微牛级电推进系统寿命试验研究现状，举例说明了目前主要应用的电推进装置寿命预测方式，包括半经验模型预测、数值模拟预测、基于数据驱动的预测以及系统层面的可靠性评估方法。对目前微牛级电推进系统的寿命评估研究现状进行总结，给出了发展思路。     

三种时频分析在复合材料无损检测中的应用

随着复合材料的广泛应用和现代信号处理技术的发展,时频分析方法已广泛应用于各类复合材料的损伤检测。将短时傅立叶变换、小波变换和黄氏变换三种时频分析技术经过Matlab编程,结合Lamb波应用于玻璃纤维增强的复合材料层合板的无损检测,并对三种时频结果进行比较与分析。结果表明：上述三种时频分析方法都能进行复合材料的损伤判定,但黄氏变换方法的分析精度更高。     

A novel signal processing approach for LEO space debris based on a fence-type space surveillance radar system

The increase in space debris can seriously threaten regular activities in the Low Earth Orbit (LEO) environment. Therefore, it is necessary to develop robust, efficient and reliable techniques to understand the potential motions of the LEO debris. In this paper, we propose a novel signal processing approach to detect and estimate the motions of LEO space debris that is based on a fence-type space surveillance radar system. Because of the sparse distribution of the orbiting debris through the fence in our observations, we formulate the signal detection and the motion parameter estimation as a sparse signal reconstruction problem with respect to an over-complete dictionary. Moreover, we propose a new scheme to reduce the size of the original over-complete dictionary without the loss of the important information. This new scheme is based on a careful analysis of the relations between the acceleration and the directions of arrival for the corresponding LEO space debris. Our simulation results show that the proposed approach can achieve extremely good performance in terms of the accuracy for detection and estimation. Furthermore, our simulation results demonstrate the robustness of the approach in scenarios with a low Signal-to-Noise Ratio (SNR) and the super-resolution properties. We hope our signal processing approach can stimulate further work on monitoring LEO space debris.     

基于运动特征和中心环绕模型的空对地车辆检测方法

平台的移动基于空基车辆检测以其视野广、速度快、适应性强等优点,近些年成为了智能交通和航空应急救援等领域内的一个热点课题。然而,在城市背景的交通环境下,由于复杂背景造成的干扰、车辆运动的不规律性以及光照等条件变化带来的噪声,有效地获取运动对象特征是空对地视觉监视方法面临的一个难题。为有效解决这一问题,提出了结合运动特征的中心环绕模型进行空对地车辆检测的算法。现有的空对地车辆检测算法,大多是针对固定的摄像头的情况下,基于运动特征进行检测,例如光流法、背景差法等。这些方法在对高速公路等背景相对较为简单的环境下获得了良好的检测结果,但当面对城市复杂背景条件或摄像头自身运动的状况时,难以有效地检测出运动目标。而在对颜色、强度等静态特征进行显著度分析的基础上,加入了对运动特征的分析,可以解决在复杂环境下准确提取车辆目标有效特征的问题。试验表明,相较于基于图像相减的检测算法和单一显著度算法,该算法可以获得更高的检测率与更低的误报率。