基于ADS-B技术的通航飞行器避险方法研究

发布时间：2018-07-21 19:49

【摘要】：通用航空作为一个高技术航空产业,对国民经济增长有巨大的推动作用。我国通用航空虽然初具规模,但随着时代的发展,将会对通用航空提出更广阔的要求。其中,运输需求的日益增加导致飞行流量上升,所以飞行冲突问题显著,威胁飞行安全。为了提高航空的安全性,出现了很多辅助监视系统,其中ADS-B就是一种新的监视技术,可以全面的提供飞行数据。相比其他监视技术,ADS-B具有高精度、数据更新快、建设成本低等优点。通航密集飞行的避险问题可以分为两个部分:飞行器的冲突探测和冲突解脱。本文采用ADS-B技术对避险问题展开了研究。首先,通过分析ADS-B的技术特点,包括数据结构、刷新频率等,来确定飞行器采用ADS-B技术的性能指标,并分析ADS-B技术应用在通用航空的优越性。其次,通过分析空域结构,建立飞行器的保护区模型。以此模型为基础给出飞行器的运动方程,建立冲突探测模型和冲突解脱模型。详细介绍了冲突探测的识别方法,阐述了飞行器冲突解脱的方法,并给出三种解脱方式-调整航向角、调整航行速度和动态冲突解脱法。然后,由于动态冲突解脱策略要求一种快速的优化算法来对关键问题求解,所以对粒子群优化算法进行了改进。由于飞行器轨迹预测的准确性受到一些不确定因素的影响,比如雷达、导弹、气候等,所以,在提出动态自适应机器粒子群算法(DARPSO)中考虑真实环境障碍等不确定因素的影响具有重要的意义。DARPSO在解决冲突解脱问题时,能够调节粒子探索和搜索能力之间的平衡,在冲突解脱问题中修改算法的参数,同时保持粒子间的多样性。通过仿真验证,所提出的算法在收敛精度和收敛速度方面有很大的提高。将所提出的DARPSO应用到冲突解脱仿真实验中,能够表现出较好的解脱效果,大大缩短解脱时间。最后,介绍了搭建实验平台的实验设备,包括ADS-B机载设备、地面站、监视软件等。实验的开展得到了石家庄飞机工业有限公司的支持,将ADS-B机载设备加装到Y5B飞机上,进行了试飞试验,试验结果显示本文所做工作对解决通航密集飞行避险问题具有很大帮助。
[Abstract]:General Aviation as a high-tech aviation industry, has a huge role in promoting the national economic growth. Although general aviation of our country takes shape initially, but with the development of the times, it will put forward broader request to general aviation. Among them, the increasing transportation demand leads to the increase of flight flow, so the problem of flight conflict is significant, which threatens flight safety. In order to improve aviation safety, there are many auxiliary surveillance systems, among which ADS-B is a new monitoring technology, which can provide flight data comprehensively. Compared with other monitoring techniques, ADS-B has the advantages of high accuracy, fast data updating and low construction cost. The flight avoidance problem can be divided into two parts: collision detection and conflict resolution. In this paper, ADS-B technology is used to study the problem of risk avoidance. Firstly, by analyzing the technical characteristics of ADS-B, including data structure and refresh frequency, the performance index of ADS-B technology is determined, and the superiority of ADS-B technology in general aviation is analyzed. Secondly, by analyzing the airspace structure, the protected area model of aircraft is established. On the basis of this model, the motion equations of the aircraft are given, and the conflict detection model and conflict resolution model are established. In this paper, the method of conflict detection is introduced in detail, and the method of collision resolution for aircraft is expounded, and three kinds of extrication methods are given: adjusting heading angle, adjusting navigation speed and dynamic conflict resolution. Then, the particle swarm optimization (PSO) algorithm is improved because the dynamic conflict resolution strategy requires a fast optimization algorithm to solve the key problems. Because the accuracy of aircraft trajectory prediction is affected by some uncertainties, such as radar, missiles, climate, etc., It is important to consider the influence of uncertain factors such as real environment obstacles in the proposed dynamic adaptive machine particle swarm optimization (DARPSO). DARPSO can adjust the balance between particle exploration and searching ability in solving the conflict resolution problem. In the conflict resolution problem, the parameters of the algorithm are modified while maintaining the diversity of particles. The simulation results show that the proposed algorithm can improve the convergence accuracy and convergence speed greatly. When the proposed DARPSO is applied to the simulation experiment of conflict resolution, it can show good relief effect and greatly shorten the extrication time. Finally, the experimental equipment for building the experimental platform is introduced, including ADS-B airborne equipment, earth station, monitoring software and so on. The experiment was carried out with the support of Shijiazhuang aircraft Industry Co., Ltd., the ADS-B airborne equipment was installed on Y5B aircraft, and the flight test was carried out. The test results show that the work done in this paper is of great help to solve the problem of navigable dense flight avoiding danger.
【学位授予单位】：河北科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：V355.1;TP18

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