无人战斗机的态势感知模型框架

提出了利用面向对象的贝叶斯网络构建无人战斗机的态势感知模型框架，探索了有利于多机协同作战的态势感知模型，以及作战环境具有多个不同威胁实体的态势感知；给出了无人战斗机态势感知的特点及威胁水平评估模型，以及构建基于多机协同的态势感知模型的具体步骤。     

面向过程控制的工程变更系统研究

针对工程变更过程中变更传播影响范围难以预测的问题,提出了一种制定变更传播路径的方法。研究分析了跟踪工程变更过程以及在跟踪基础上进行协同感知的策略,有效解决了目前企业工程变更过程中存在的不完整、不可控和不一致的问题。最后创建了一种基于过程控制的工程更改控制系统,实现了信息共享、过程控制、过程分析和协同感知。     

直升机前视地形告警算法研究

为了更好地利用直升机地形感知与告警系统,减少可控飞行撞地事故的发生,提出了一种适用于直升机地形感知与告警系统的前视地形告警算法.首先介绍了直升机前视地形告警的基本原理,并给出了前视告警包线的设计流程;然后提出了前视告警包线下视边界、前视边界、上视边界和横侧边界的确定方法;最后构建仿真算例进行了仿真验证.仿真结果表明,提出的算法可以在直升机飞行撞地前提前预警.     

一种基于感知的大规模场景粒子系统绘制方法

在分析已有的大规模场景下使用粒子系统对自然现象等模拟方法的基础上,提出了一种绘制大规模粒子系统的方法.在大规模粒子系统的绘制过程中引入基于视截体模型的感知算法对粒子进行细化并引入双重缓冲技术以及粒子动态纹理.将粒子按所属不同区域进行分层次绘制,提高了真实性和灵活性,最终解决了传统绘制方法不适用大规模粒子系统的问题.实验表明,系统的模拟效果真实,满足系统对实时性的要求,适合大规模粒子系统的绘制.     

地面战场传感侦察系统及其发展概述

地面战场传感侦察系统集传感器技术、信号与信息处理技术、无线网络传输技术为一体,是网络中心战的重要信息基石。对美军地面战场传感侦察系统的研究现状及其发展趋势作出了分析。     

文体学在英语专业高年级写作教学中的应用

写作文体的得体性是衡量写作质量的一个重要标准,本文从英语写作过程中需要考虑的措辞、句法和语篇三个层面将文体学的理论和分析方法引入英语专业高年级写作教学活动中,旨在培养学生的文体意识,促进写作教学。     

浅析英语写作教学中跨文化意识的培养

在传统的写作教学中,英语教师往往只注重学生语言层面的培养,却很少在文化层面上分析中英两种语言的差异,以至于很多学生虽然能够很好的掌握词汇和语法知识,却总是用汉语的思维习惯去进行英语表达,结果写作水平很难得到提高。     

Near-optimal geostationary transfer maneuvers with cooperative en-route inspection using hybrid optimal control

This research investigates the performance of bi-level hybrid optimal control algorithms in the solution of minimum delta-velocity geostationary transfer maneuvers with cooperative en-route inspection. The maneuvers, introduced here for the first time, are designed to populate a geostationary constellation of space situational awareness satellites while providing additional characterization of objects in lower-altitude orbit regimes. The maneuvering satellite, called the chaser, performs a transfer from low Earth orbit to geostationary orbit, during which it performs an inspection of one of several orbiting targets in conjunction with a ground site for the duration of the target?s line-of-site contact with that site. A three-target scenario is used to test the performance of multiple bi-level hybrid optimal control algorithms. A bi-level hybrid algorithm is then utilized to solve fifteen-, and thirty-target scenarios and shown to have increasing benefit to complete enumeration as the number of targets is increased. Results indicate that the en-route inspection can be accomplished for a small increase in the delta-velocity required for a simple transfer to geostationary orbit given the same initial conditions.     

任务复杂度对自动化意识的影响

现代飞机驾驶舱的高度自动化容易导致飞行员对自动化的自满和依赖,进而降低飞行员的自动化意识。自动化意识失效是飞机事故征候和事故的主要影响因素之一。通过研究任务复杂度对自动化意识的影响以及自动化意识失效的原因后发现,高复杂度任务下的自动化意识失效概率显著高于低复杂度任务,对自动化的注意力分配不足和自动化自满是高复杂度任务下自动化意识失效的两个主要原因。研究表明,在高负荷阶段,如果自动化失效,人难以在短期内发现自动化失效和作出有效响应。因此,增强自动化意识是维持航空安全的重要保障。     

A Partially Orthogonal EnKF approach to atmospheric density estimation using orbital debris

A key requirement for accurate trajectory prediction and space situational awareness is knowledge of how non-conservative forces affect space object motion. These forces vary temporally and spatially, and are driven by the underlying behavior of space weather particularly in Low Earth Orbit (LEO). Existing trajectory prediction algorithms adjust space weather models based on calibration satellite observations. However, lack of sufficient data and mismodeling of non-conservative forces cause inaccuracies in space object motion prediction, especially for uncontrolled debris objects. The uncontrolled nature of debris objects makes them particularly sensitive to the variations in space weather. Our research takes advantage of this behavior by utilizing observations of debris objects to infer the space environment parameters influencing their motion.The hypothesis of this research is that it is possible to utilize debris objects as passive, indirect sensors of the space environment. We focus on estimating atmospheric density and its spatial variability to allow for more precise prediction of LEO object motion. The estimated density is parameterized as a grid of values, distributed by latitude and local sidereal time over a spherical shell encompassing Earth at a fixed altitude of 400 km. The position and velocity of each debris object are also estimated. A Partially Orthogonal Ensemble Kalman Filter (POEnKF) is used for assimilation of space object measurements to estimate density.For performance comparison, the scenario characteristics (number of objects, measurement cadence, etc.) are based on a sensor tasking campaign executed for the High Accuracy Satellite Drag Model project. The POEnKF analysis details spatial comparisons between the true and estimated density fields, and quantifies the improved accuracy in debris object motion predictions due to more accurate drag force models from density estimates. It is shown that there is an advantage to utilizing multiple debris objects instead of just one object. Although the work presented here explores the POEnKF performance when using information from only 16 debris objects, the research vision is to utilize information from all routinely observed debris objects. Overall, the filter demonstrates the ability to estimate density to within a threshold of accuracy dependent on measurement/sensor error. In the case of a geomagnetic storm, the filter is able to track the storm and provide more accurate density estimates than would be achieved using a simple exponential atmospheric density model or MSIS Atmospheric Model (when calm conditions are assumed).