A multi-aircraft conflict detection and resolution method for 4-dimensional trajectory-based operation

Conflict Detection and Resolution(CDR) is the key to ensure aviation safety based on Trajectory Prediction(TP). Uncertainties that affect aircraft motions cause difficulty in an accurate prediction of the trajectory, especially in the context of four-dimensional(4D) Trajectory-Based Operation(4DTBO), which brings the uncertainty of pilot intent. This study draws on the idea of time geography, and turns the research focus of CDR from TP to an analysis of the aircraft reachable space constrained by 4D waypoint constraints. The concepts of space–time reachability of aircraft and space–time potential conflict space are proposed. A novel pre-CDR scheme for multiple aircraft is established. A key advantage of the scheme is that the uncertainty of pilot intent is accounted for via a Space-Time Prism(STP) for aircraft. Conflict detection is performed by verifying whether the STPs of aircraft intersect or not, and conflict resolution is performed by planning a conflict-free space–time trajectory avoiding intersection. Numerical examples are presented to validate the efficiency of the proposed scheme.     

Manifold structure preservative for hyperspectral target detection

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.     

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C₄ index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.     

基于MapReduce的CME参数识别模型并行计算技术

日冕物质抛射（Coronal Mass Ejection，CME）参数识别模型是太阳风预报过程的重要组成部分.在空间环境预报业务中，为提高太阳风预报的准确率，需要提高CME参数识别的精度.模型以计算任务串行的方式运行，运算效率低导致模型运算时间长，不能满足这种需求.CME参数识别模型的物理运算过程相互不独立，其在单节点上的运行方式不能满足并行化要求.基于MapReduce的并行计算框架，改进了CME参数识别模型的计算流程，提出CDMR（CME detection under MapReduce）方法，实现了CME参数识别模型的并行计算，并对比分析CME参数识别模型在串行计算和MapReduce并行计算下的运行时间，提高了模型的识别精度和计算效率.      

某机载探测系统自主回收技术的实现

针对某机载探测系统探头回收自动化程度低的问题，提出了在无需操作员干预情况下实现探头自主回收的技术方法。采用了先进的控制算法，将缆位角与探头、缆长进行结合并引入了绞车控制系统，使绞车对探头的回收速度及缆长可进行实时调整，并给出了3种不同缆位角摆动条件下缆长与探头回收速度的控制曲线关系。研发了自主回收装置，设计了能够实现自主回收功能的探头回收机构。实验验证表明：在无需人为干预的条件下，在缆位控制系统及自主回收机构的配合下，系统实现了对探头快速、安全的回收。     

夜气辉对天基光学碎片探测的影响分析

设计并完成了夜气辉背景下的天基碎片观测试验，获取了综合谱段的观测数据。基于观测数据进行夜气辉的辐射特性及其对天基碎片探测的影响分析。结果表明，夜气辉辐射随纬度和高度呈现一定的分布特点，这些特点均符合夜气辉的理论分布规律。在约90 km处辐射最强，亮度可达到单位积分时间每平方角秒近15星等，这对恒星及碎片提取概率影响较大，其中，13等星的提取概率仅为40.5%。     

基于相对最值点的最小二乘直线检测方法

为了解决传统目标检测中遇到的检测速度慢、精度低等问题，本文提出一种新的图像特征——相对最值点，并依据相对最值点提出一种新的最小二乘直线检测的方法。该方法首先搜索Canny边缘的闭合包络，并寻找闭合包络的相对最值点。包络上相邻相对最值点之间长度满足阈值要求的即为疑似直线的两端点，进而利用最小二乘拟合方法获得该疑似直线的拟合方差，最后利用拟合方差与拟合直线长度之比作为直线判定的标准用来检测出图像中的直线。实验表明，本方法与传统方法如PPHT直线检测、LSD直线检测方法等相比，检测直线的精度更高，检测速度更快。     

基于类脑模型与深度神经网络的目标检测与跟踪技术研究

目标检测与跟踪技术广泛应用于交通、医疗、安保和航天等领域.目前，目标检测与跟踪技术面临目标微弱、背景复杂、目标被遮挡等挑战.同时，随着脑科学研究的不断深入，人们对人脑视觉系统的理解逐渐透彻，利用类脑计算解决复杂背景下高精度目标检测与跟踪问题成为相关领域的重要研究方向.本文结合神经工程导向的类脑模型和计算机工程导向的深度神经网络(Deep Neural Networks, DNNs)，提出多种基于类脑模型与深度神经网络的目标检测与跟踪算法，包括：基于演算侧抑制的目标检测算法，基于结构 对比度(Structure Contrast, SC)视觉注意模型的弱小目标检测算法和基于记忆机制与分层卷积特征的目标跟踪算法.实验结果表明，将类脑模型和深度神经网络应用于目标检测和跟踪领域，有利于实现复杂条件下的高精度目标检测和鲁棒性目标跟踪.     

基于单应性矩阵的模型迎角单目视频测量方法

风洞试验中模型迎角的精准测量是降低阻力系数误差的重要途径之一，为此，提出了基于单应性矩阵的模型迎角单目视频测量方法。该方法通过两个单应性矩阵，获取试验过程中相机实时位姿和标记点物方空间位置坐标，应用坐标旋转关系，完成试验模型的迎角测量。数值仿真试验结果表明：迎角测量误差与待测标记点到风洞壁板间的距离偏差近似为线性关系，因此，当标记点不满足共面条件时，可根据该特点进行测量误差修正。静态标定和风洞迎角测量试验结果表明：修正系统误差后，迎角实测数据的测量准度在0.01°以内，精度不超过0.012°。本文方法易于实施，工程实用价值强。     

基于马鞍形磁场的永磁直线电机位置解算方法

针对直线电机转子位置检测低成本和较高精度的要求,提出了一种基于马鞍形磁场来获得位置信息的方法及实现过程。分析了永磁体表面的磁场分布特征,指出了利用马鞍形磁场分布曲线来解算位置信息的优点,提出了根据马鞍形磁场推算位置信息的算法流程:先测得错开相位的2条马鞍形曲线数据,划分为4个象限后建立数据库；位置检测时,运用区间搜索算法寻得实际角度所在区间；再根据精度要求,采用合适的插值算法计算得到待解算的角度。仿真结果表明:基于马鞍形磁场的位置解算,能较好地得到直线电机转子的位置信息。