ADS-B机场场面监视技术研究

如何在复杂的机场环境和恶劣的气象条件下管理日益增多的飞机和地面服务车辆,成为机场运行管理中必须考虑的问题。本文给出了采用广播式自动相关监视系统(ADS-B)技术实现机场场面监视的解决方案。     

基于数据融合的ADS-B/ACARS空域监视系统设计

研究了广播式自动相关监视(ADS-B)与飞机通信寻址报告系统(ACARS)两种数据链路的优缺点;提出了以ADS-B监视为主,将两种数据链路数据融合的思想.研究数据融合算法,构建数据融合模型,并编程实现了ADS-B/ACARS组合空域监视软件.现场运行证明,系统增大了多源监视范围、丰富了监视信息、提高了监视精度.     

直升机加改装ADS-B设备的研究

论述了直升机加改装ADS-B设备以适应FSS监管要求的必要性,并以恩斯特龙直升机为例,详细介绍了天线和同轴电缆对系统性能的影响,以及低成本过渡方案的设计和实施。     

Actual TDoA-based augmentation system for enhancing cybersecurity in ADS-B

Currently, cybersecurity and cyber resilience are emerging and urgent issues in next-generation air traffic surveillance systems, which depend primarily on Automatic Dependent Surveillance-Broadcast (ADS-B) owing to its low cost and high accuracy. Unfortunately, ADS-B is prone to cyber-attacks. To verify the ADS-B positioning data of aircraft, multilateration (MLAT) techniques that use Time Differences of Arrivals (TDoAs) have been proposed. MLAT exhibits low accuracy in determining aircraft positions. Recently, a novel technique using a theoretically calculated TDoA fingerprint map has been proposed. This technique is less dependent on the geometry of sensor deployment and achieves better accuracy than MLAT. However, the accuracy of the existing technique is not sufficiently precise for determining aircraft positions and requires a long computation time. In contrast, this paper presents a reliable surveillance framework using an Actual TDoA-Based Augmentation System (ATBAS). It uses historically recorded real-data from the OpenSky network to train our TDoA fingerprint grid network. Our results show that the accuracy of the proposed ATBAS framework in determining the aircraft positions is significantly better than those of the MLAT and expected TDoA techniques by 56.93% and 48.86%, respectively. Additionally, the proposed framework reduced the computation time by 77% compared with the expected TDoA technique.     

ADS-B用于高精度雷达标定的方法

为了更好地解决高精度雷达标定的问题,提出了基于广播式自动相关监视系统(ADS-B)固定误差及目标回波中心动态修正的雷达标定新方法。首先分析了ADS-B位置数据误差的来源、类型及在雷达坐标系下的特征,同时对民航目标回波中心的变化作了分析建模,在此基础上进一步通过对雷达数据与ADS-B数据之差作动态联合修正,最终估算出雷达系统误差,提高了雷达系统误差标定的精度和稳定性。并利用多批次的实测数据对该标定新方法与其他方法进行了对比验证,结果表明,该方法有效提高了标定的精度和稳定性,并已成功应用于雷达标定设备中。     

ADS-B数据链的比较及特性研究

ADS-B以先进的地空/空空数据链为通信手段,已成为未来主要的航空监视手段之一。本文介绍了ADS-B的三种数据链的结构和特性,并对三种数据链技术作了比较分析,提出了一种多数据链融合综合显示的模型和实现基于多数据链的直接空-空监视的方法。     

1090ES广播式自动相关监视系统的CPR算法

在1090ES广播式自动相关监视（ADS—B）系统中,为了对飞机的位置消息进行有效编码,经度和纬度采用简洁位置报告（CPR）的形式。介绍了CPR编解码算法的基本原理,详细描述了1090ESADS—B系统中经度和纬度消息的CPR编解码方法,给出了全球位置和本地位置的编解码算法和实现步骤。     

基于ADS-B的航空器测高系统误差评估方法

针对基于广播式自动相关监视(ADS-B)数据的航空器高度保持性能监控中测高系统误差准确评估问题,提出了基于核平滑和混合正态分布模型的数据平滑和拟合分析方法。提出了ADS-B信息低分辨率高度数据的核平滑方法,通过比较分析得到了数据平滑的最优窗宽,基于实测数据的分析证明了该方法对于航空器测高系统误差评价的有效性。针对航空器使用的2套独立的测高学设备具有不同的高度保持性能分布特征的问题,提出了基于混合正态分布模型的拟合分析方法,实测结果表明,该方法可以准确拟合航空器测高系统误差的双峰特征。本文所提出的方法已经在中国RVSM航空器高度保持性能的实际分析中使用,分析结果满足国际民航组织的相关要求。     

Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area.