基于改进蚁群算法的改航路径规划研究

当航班遇到恶劣天气或者飞行冲突时,可以采取航班改航策略为需要改航的航班规划出合适的飞行路径。采用栅格法对环境进行建模并且对传统蚁群算法进行改进。首先,在转移概率的计算上加入危险天气的影响等级;其次,在节点选择上采用贝叶斯思想,通过后验概率对候选节点进行评估;最后,通过在短时间内信息素的增量上的差别来增大较优路径和其他路径之间的信息素量的差距,引导算法收敛到最优路径,同时加快算法的收敛速度。经算例分析证明了方法的有效性和可行性。     

基于改进A-Star算法的隐身无人机快速突防航路规划

针对现代战争中隐身无人机（UAV）在高严密的组网雷达防御体系下的生存及突防问题,提出了基于改进A-Star算法的隐身无人机战区突防航路规划技术。首先对隐身无人机突防过程进行了分析建模,分别建立了隐身无人机的运动学模型、动态雷达散射截面特性和组网雷达探测概率的计算模型。然后针对传统算法在解决隐身突防问题时的不足,充分考虑所规划航路时的快速性和安全性要求,设计了改进A-Star算法。在算法中引入了多层变步长搜索策略和无人机的姿态角信息,结合秩K融合准则,通过每段航迹上隐身无人机被组网雷达系统的发现概率来判断新航迹点的可行性。仿真结果表明,改进A-Star算法能够在复杂的组网雷达系统下快速生成更优的战区突防航路,具有一定的应用价值。     

工程施工中深基坑高程放样的误差分析

论述了市政工程施工中深基坑高程放样方法,对放样过程中产生的误差及误差的大小进行了分析。本测量放样方法可以满足给排水工程施工,以及符合高程精度要求的验收规范。     

引入案例教学方法改进空域规划课程教学

《空域规划》课程是空中交通管理专业的一门专业必修课,具有很强的实践性和研究实用性。通过介绍案例教学方法,阐述案例教学方法在空域规划课程中的应用,讨论案例教学时应注意的问题。引入案例式教学方法,加强理论知识的理解,提高学生的实践能力,增强学生的学习兴趣,取得较为满意的教学效果,为深化空域规划课程改革打下一定的理论基础。     

Collective trajectory planning for satellite swarm using inter-satellite electromagnetic force

The inter-satellite electromagnetic force presents several significant advantages that help to expand its space applications to the multi-satellite missions gradually. For a satellite swarm, whether the electromagnetic force is applied to enable collective maneuver and how to provide a better performance and broader applications for such swarm highlight an important issue. Considering the trajectory planning problem of satellite swarm using inter-satellite electromagnetic force, a behavior-based collective planning scheme is developed by designing the desired velocity of each satellite as the sum of several different behavioral contributions, which are used to represent the internal and external interactions of the swarm. Therefore, the desired configuration is associated with the equilibrium points of the pre-designed kinematical field. Furthermore, the trajectory planning problem could be translated to a parameter optimization problem considering the swarm dynamics with inter-satellite electromagnetic force. Then based on the analysis of the applicability and advantages triggered by integrating the inter-satellite electromagnetic force into such behavior-based planning scheme, the collective trajectory planning problem with sole electromagnetic force actuation and hybrid actuation with thruster are studied respectively. Numerical simulations are carried out to verify the validity of the proposed algorithm, and the satellite swarm performance enhanced by inter-satellite electromagnetic force is discussed at last.     

A satellite schedulability prediction algorithm for EO SPS

With notably few exceptions, the existing satellite mission operations cannot provide the ability of schedulability prediction, including the latest satellite planning service (SPS) standard-Sensor Planning Service Interface Standard 2.0 Earth Observation Satellite Tasking Extension (EO SPS) approved by Open Geospatial Consortium (OGC). The requestor can do nothing but waiting for the results of time consuming batch scheduling. It is often too late to adjust the request when receiving scheduling failures. A supervised learning algorithm based on robust decision tree and bagging support vector machine (Bagging SVM) is proposed to solve the problem above. The Bagging SVM is applied to improve the accuracy of classification and robust decision tree is utilized to reduce the error mean and error variation. The simulations and analysis show that a prediction action can be accomplished in near real-time with high accuracy. This means the decision makers can maximize the probability of successful scheduling through changing request parameters or take action to accommodate the scheduling failures in time.     

基于PH曲线的多无人飞行器协同航迹规划研究

随着无人飞行器智能化的发展,如何使其在动态环境下避开运动威胁,并能协同以规定的时间和角度进行攻击,从而顺利完成作战任务成为研究热点.基于Pythagorean Hodograph(PH)曲线进行在线航迹规划,提出了分布估计算法和差分进化算法相结合的方法进行航迹参数寻优,并给出了在线避障多无人飞行器协同航迹规划方法,仿真试验结果表明加入速度预测的多无人飞行器协同航迹规划方法的有效性.     

基于GA-OCPA学习系统的无人机路径规划方法

为解决未知空域中无人机路径规划方法实时性和适用性不足的问题,以生物应激条件反射理论为基础,将无人机实时路径规划类比为在外界条件刺激下的一种自学习行为。首先,将概率自动机与遗传算法相结合,设计了基于Skinner操作条件反射理论框架(GA-OCPA)的学习系统;然后,将无人机规避机动的飞行速度、滚转加速度和拉升加速度作为系统学习的行为,并计算每次学习尝试之后的选择概率和个体适应度,通过遗传算法搜索最优行为进而得到最优路径;最后,运用增量多层判别回归树(IHDR)对学习得到的最优行为建立知识库,形成威胁状态与路径规划的匹配映射。实验结果表明GA-OCPA学习系统对于无人机路径规划具备有效性和适用性。     

用层次分析法实现航天发展目标决策

层次分析法是一种新的、实用的、多因素多目标决策方法。航天技术的发展目标的评价与优选是一个难以定量的多因素决策问题。本文将通过实例来介绍如何用层次分析法来评价航天技术发展的候选目标,决定目标发展的优先次序。本文所提出的模型仅需作适当改进,即可用于解决我国航天技术发展中面临的实际问题。