基于FlightGear的A-SMGCS场面活动三维仿真

针对A-SMGCS系统场面活动三维仿真问题,提出一种以开源模拟飞行器FlightGear为场景仿真平台,以实时ADS-B监视数据为驱动,以Linux为操作平台的机场场面活动三维仿真系统设计新方法。仿真系统按照实际机场模型进行机场布局设计,飞机、航站楼、塔台三维建模,地形数据生成,对真实机场环境进行了完整建模。由于ADS-B监视数据缺少飞机姿态信息,提出一种根据位置数据推算出姿态信息的新方法。首先把ADS-B监视数据进行航迹卡尔曼滤波,然后根据飞机前后两个位置的空间连线矢量计算飞行姿态。通过FlightGear多机网络数据接口导入飞机定位数据与姿态数据驱动飞机模型运动,实现了对飞机运动的六自由度仿真。仿真结果表明,该系统能逼真、准确再现真实机场飞机实时活动情况。由于完全基于开源软件设计,该系统低成本实现了对场面活动的实时三维仿真。     

Estimation fusion with radar and ADS-B for air traffic surveillance

The surveillance sensor that has been mainly used for target tracking in air traffic control (ATC) environment is radar. The automatic dependent surveillance — broadcasting (ADS-B), which is based on the technologies of global navigation satellite systems, is recently participating in ATC systems. Although ADS-B provides more accurate measurements than does radar, it needs careful considerations for the application of the ATC systems. This is due to the fact that the reliability of ADS-B measurements is dependent upon each aircraft whereas that of radar is not. This study proposes a system for the estimation fusion of multiple heterogeneous sensors, which includes radar and ADS-B, whose measurements and sensor characteristics are different from one another. A centralized fusion architecture based on three-dimensional earth-centered earth-fixed (ECEF) common coordinate system is adopted to process the data received asynchronously from multiple heterogeneous sensors. In case of the ADS-B, the validity of the sensor data for each aircraft is checked using not only the accuracy and integrity information of the aircraft, but also a comparison of the ADS-B data with the radar data. This study also proposes variable-sized measurement vectors and matrices for the tracking filter in order to dynamically reflect the availability of the additional measurements from the downlinked aircraft parameters (DAP) which can be obtained from mode-S radar and ADS-B. The simulation results indicate that the proposed fusion system can improve the tracking performance with the advantages of different types of surveillance sensors.     

基于均衡更新蚁群算法的飞机排序调度

飞机排序调度问题是空中交通管制的一个关键问题,本文在给出飞机排序调度模型的基础上,提出一种均衡更新蚁群算法,利用当前解与全局最优解的差异来均衡地更新信息素,增强算法的全局搜索能力,从而生成更优解。实验结果表明,均衡更新蚁群算法求解飞机排序调度问题时,能用较短时间求出优于对比算法的结果,其性能可以提高12.9%,有助于空中交通管制人员根据实时情况安排合适的飞机着陆顺序。     

A real-time dynamic optimal guidance scheme using a general regression neural network

This paper presents an investigation into the challenges in implementing a hard real-time optimal non-stationary system using general regression neural network (GRNN). This includes investigation into the dynamics of the problem domain, discretisation of the problem domain to reduce the computational complexity, parameters selection of the optimization algorithm, convergence guarantee for real-time solution and off-line optimization for real-time solution. In order to demonstrate these challenges, this investigation considers a real-time optimal missile guidance algorithm using GRNN to achieve an accurate interception of the maneuvering targets in three-dimension. Evolutionary Genetic Algorithms (GAs) are used to generate optimal guidance training data set for a large missile defense space to train the GRNN. The Navigation Constant of the Proportional Navigation Guidance and the target position at launching are considered for optimization using GAs. This is achieved by minimizing the miss distance and missile flight time. Finally, the merits of the proposed schemes for real-time accurate interception are presented and discussed through a set of experiments.     

基于自生成深度神经网络的4D航迹预测

针对四维（4D）航迹预测的实时性不强和存在迭代误差的问题,提出了一种自动生成的条件变分自动编码器（AutoCVAE）。它以编码-解码的形式直接对未来一段时间的航迹进行预测,并能灵活选取观测点个数和预测步长。该方法以处理后的广播式自动相关监视（ADS-B）数据为引导,以减小预测误差为目标,通过贝叶斯优化的方法,在预定义的搜索空间内进行模型结构搜索,每一次的超参数取值都会参考之前的评估结果,使得每一次的模型结构都能向目标更靠近一点,最终实现了一个基于ADS-B数据的高精度的4D航迹预测模型。实验得出,所提模型能快速准确地进行航迹的实时预测,其中经纬度平均绝对预测误差（MAE）均小于0.03°,高度MAE小于30 m,各时刻点的时间误差也不会超过10 s,每次批量预测轨迹的延迟时间不超过0.2 s。     

无人机三维航路动态规划及导引控制研究

为研究无人机三维航路动态规划,提出了一种工程实用性强的A*三维航路优化算法,将无人机的机动性能、飞行航程、飞行高度等约束条件有效分割到解空间。为了加快搜索速度,引入启发式权重系数对搜索策略进行改进,利用加权值自适应方法对算法的评价函数进行设计,提高航迹点搜索效率,并设计了导引控制律,基于优化算法获取的航路,能够使无人机很好地跟随规划的最优路径,同时生成的期望控制指令充分考虑了无人机本身的机动性能以及实时性要求,解决了航迹规划与航迹跟踪之间的问题,最后进行了仿真验证,结果表明,该方法是可行和有效的,有着较高的优化效率;易于实现,工程实用性强。     

A branch and cut heuristic for a runway scheduling problem

The paper is focused on one of the major air traffic management problem that consists in sequencing and scheduling airplanes landing and taking off on a runway. This difficult practical task is still carried out by flight controllers manually with little help from decision support systems. In this paper we propose an approach based on a time indexed integer programming formulation. The formulation is solved with a branch and cut method combined with some heuristic rules for dimension reduction. The effectiveness of the proposed approach is illustrated by computational experiments on real-life problem instances for the Milano Linate airport.     

基于注意力机制的CNN-LSTM的ADS-B异常数据检测

广播式自动相关监视(ADS-B)是民航新一代空中交通管理系统的重要组成部分,由于协议没有数据加密和认证,导致容易受到数据攻击.为了准确检测ADS-B数据攻击,基于ADS-B数据的时序性,提出了一种基于注意力机制的卷积神经网络-长短期记忆网络(convolutional neural networks-long short-term memory, CNN-LSTM)异常数据检测模型.首先,利用CNN提取ADS-B数据的特征,然后以时序形式将特征向量输入到LSTM中,最后使用注意力机制进行网络参数优化,实现对ADS-B数据的预测,通过计算预测误差,来进行异常检测.实验表明,该模型能够很好地检测出模拟的4种类型的异常数据,与其他机器学习方法相比,具有更高的准确率和F1分数.     

Joint optimization for area traffic control and network flow

A joint optimization problem for solving area traffic control and network flow is investigated. A bilevel programming is used to formulate this joint optimization problem where the network flow following Wardrop's principles can be obtained by solving traffic assignment problems. In this paper, we present a solution approach for jointly optimizing the area traffic control and network flow on the basis of a newly presented algorithm for concurrent flow (Comput. Oper. Res. (2004) in press). We propose three kinds of formulations for this joint optimization problem and present a gradient-based method to effectively solve this problem via a mixture of locally optimal search and global search heuristic where a near global optimum may be found. Numerical comparisons are made for the values of performance index achieved by the joint optimization problem with system optimal flow and those did by equilibrium flow at various sets of initial signal settings. Substantially good results have demonstrated the robustness of the proposed algorithm in solving both system optimal and user equilibrium flow for the joint optimization problem at large-scale networks.     

飞行数据处理系统的设计与实现

随着我国航空事业的飞速发展,飞行数据处理系统成为新一代空中交通管制系统的一项重要支持系统。针对新一代飞行数据处理系统的功能特征和飞行情报具有复杂多样性和实时性的特点,采用模块化程序设计思想设计实现新一代的飞行数据处理系统。     

On-line free-flight path optimization based on improved genetic algorithms

Free flight (FF) is the ideal strategy of current investigations on air traffic management systems, where an on-line flight path optimization algorithm is of top importance. This paper proposes an innovative algorithm with potential real-time properties for FF path optimization, by using an improved genetic algorithm (GA). Two kinds of mathematical models for the on-line flight path optimization problem are proposed to cover the near and far future applications. Several improvements are introduced to the GA to speed up its convergence as well as to improve performance. Simulation results show that the new algorithm is effective and has potential to solve the on-line FF path optimization problem in real time.     

改进型空中交通流量预测算法的验证与实现

为了提升管制现场运行数据挖掘效率,改善现有空管流量预测系统存在系统结构与算法设计复杂性,提出一种基于空中交通管制自动化系统综合航迹数据和基础飞行数据交换报文处理的,能够实时修正航空器航迹4D剖面预测模型的流量预测算法。主要通过计划航迹和雷达航迹匹配关联算法模型以及利用航向角平面投影匹配算法提高流量预测的精确性。通过与当前主流的优秀算法对比和验证,方法能够全面、真实反映目标航迹当时运动态势和管制区域流量预测信息,工程实现较为简洁,具有一定的工程实践意义。     

Artificial Situation Awareness for Increased Autonomy of Unmanned Aerial Systems in the Terminal Area

Situation awareness is the human function of perceiving, comprehending and projecting the state of the environment which is of critical importance to the safe operation of aircraft. A highly autonomous Unmanned Aerial System (UAS) must replicate this behaviour in order to maintain an acceptable level of safety verses a manned vehicle. Nowhere in the flight is situation awareness more critical than during operation in the terminal area. Of primary concern during this stage of flight is the awareness of other traffic heading for the same airfield. This paper presents of a novel method of spatial projection of traffic vehicles encountered by an autonomous UAS in the terminal stage of flight. This projection method relies on a cooperative means of traffic perception, such as Automated Dependant Surveillance - Broadcast (ADS-B) and assumes there is a predefined route which vehicles follow through the terminal region. Whilst this is the case at the majority of airfield, traffic vehicles will not follow this path perfectly. This uncertainty in path following accuracy is captured by utilising a curvilinear reference frame and dealing with discrete transitions (such as the initiation of a turn) separately. It is shown that whilst this technique increases the computational complexity of the problem it can offer significant performance benefit.     

Optimizing urban traffic light scheduling problem using harmony search with ensemble of local search

This study addresses urban traffic light scheduling problem (UTLSP). A centralized model is employed to describe the urban traffic light control problem in a scheduling framework. In the proposed model, the concepts of cycles, splits, and offsets are not adopted, making UTLSP fall in the class of model-based optimization problems, where each traffic light is assigned in a real-time manner by the network controller. The objective is to minimize the network-wise total delay time in a given finite horizon. A swarm intelligent algorithm, namely discrete harmony search (DHS), is proposed to solve the UTLSP. In the DHS, a novel new solution generation strategy is proposed to improve the algorithm’s performance. Three local search operators with different structures are proposed based on the feature of UTLSP to improve the performance of DHS in local space. An ensemble of local search methods is proposed to integrate different neighbourhood structures. Extensive computational experiments are carried out using the traffic data from partial traffic network in Singapore. The DHS algorithm with and without local search operators and ensemble is evaluated and tested. The comparisons and discussions verify the effectiveness of DHS algorithms with local search operators and ensemble for solving UTLSP.     

Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft

The development of an automated health monitoring framework is critical for aviation system safety, especially considering the expected increase in air traffic over the next decade. Conventional approaches such as model-based and exceedance methods have a low detection accuracy and are limited to specific applications. This paper proposes a robust real-time health monitoring framework for detecting performance anomalies, which may impact system safety during flight operations, with high accuracy and generalized applicability. The proposed monitoring framework utilizes sensor data from commercial flight data recorders to predict possible flight performance anomalies. Decimation, a signal processing technique, in conjunction with Savitzky-Golay filtering is utilized to preprocess the dataset and mitigate sampling rate and noise issues that prevent direct usage of historical flight data. Correlation-based feature subset selection is subsequently performed, and these features are used to train a support vector machine that predicts flight performance. With this model, performance anomalies in the test data are automatically detected based on deviations from the predicted flight behavior. The proposed monitoring framework was demonstrated to detect performance anomalies in real-time and exhibited accurate detection capabilities with high computational efficiency.     

Comparing real-time and incremental heuristic search for real-time situated agents

Real-time situated agents, such as characters in real-time computer games, often do not know the terrain in advance but automatically observe it within a certain range around themselves. They have to interleave searches with action executions to make the searches tractable when moving autonomously to user-specified coordinates. The searches face real-time requirements since it is important that the agents be responsive to the commands of the users and move smoothly. In this article, we compare two classes of fast heuristic search methods for these navigation tasks that speed up A* searches in different ways, namely real-time heuristic search and incremental heuristic search, to understand their advantages and disadvantages and make recommendations about when each one should be used. We first develop a competitive real-time heuristic search method. LSS-LRTA* is a version of Learning Real-Time A* that uses A* to determine its local search spaces and learns quickly. We analyze the properties of LSS-LRTA* and then compare it experimentally against the state-of-the-art incremental heuristic search method D* Lite on our navigation tasks, for which D* Lite was specifically developed, resulting in the first comparison of real-time and incremental heuristic search in the literature. We characterize when to choose each one of the two heuristic search methods, depending on the search objective and the kind of terrain. Our experimental results show that LSS-LRTA* can outperform D* Lite under the right conditions, namely when there is time pressure or the user-supplied h-values are generally not misleading.     

低空突防导航实时仿真测试系统研究

为了低空飞行突破敌人防线,现代先进飞机配有低空突防导航设备导引飞行员实现贴地飞行和低空障碍回避;然而,低空突防导航设备的试飞试验不仅风险极高,而且代价高昂、试验周期长;文中构建了低空突防导航实时仿真测试系统,给出了仿真测试系统的结构设计、关键技术设计,并基于此系统对突防导航算法进行充分仿真测试,大大减少了实际试飞验证的次数,从而降低了试飞风险和试飞成本。     

国际航线网络中K条最短路径算法改进与仿真

K条最短路径(KSP)问题是国际航线网络实际路径优化问题。通过对航线网络特征与K条最短路径算法的分析,研究了解决KSP问题的典型Yen算法。针对Yen算法求解候选路径占用大量运算时间的问题,提出一种改进Yen算法。改进Yen算法通过借助A*算法的启发式策略,减少了产生候选航线路径的时间,从而提高了算法的搜索效率并减小了算法搜索的规模。通过对国际航线网络实例的仿真,实验结果表明改进Yen算法能够快速求解国际航线网络中的KSP问题;同时,与Yen算法相比,运算效率提升了75.19%以上,能够为航线路径优化提供决策支持。     

航班恢复问题的迭代局部搜索算法

在恶劣天气和机械故障等原因造成航班不能按照原计划执行时，航空公司需要采取相应的措施对航班进行恢复。本文基于经典的资源指派模型，综合考虑了调整时间、换机、联程拉直、取消航班和调机5种恢复策略，提出一种以最小化加权成本为优化目标的航班恢复模型，并设计一种迭代局部搜索算法。首先用构造-修复启发式方法构造可行解，然后从该初始解出发，在飞机路线对的邻域中进行局部搜索。当陷入局部最优后，对解进行扰动，然后从扰动后的解重新出发进行局部搜索。为了提高搜索效率，同时降低陷入局部最优解的概率，局部搜索过程采用模拟退火算法。实例结果表明，本文提出的模型及算法能够在短时间内对受到影响的大规模航班计划进行恢复。     

一种实时多目标路径规划方法

提出一种利用实时搜索思想的多目标路径规划方法.首先设计并实现局部路径规划算法,在有限的局部空间内执行启发式搜索,求解所有局部非支配路径;在此基础上,提出实时多目标路径规划方法,设计并实现相应的启发式搜索算法,在线交替执行局部搜索过程、学习过程与移动过程,分别用于求解局部空间内的最优移动路径,完成状态的转移和更新状态的启发信息,最终到达目标状态.研究表明,实时多目标启发式搜索算法通过限制局部搜索空间,避免了大量不必要的计算,提高了搜索效率,能够高效地求解多目标路径规划问题.