运载火箭遥测数据智能判读算法应用

在运载火箭高发射密度、高判读需求、高数据量的背景下，现有自动化判读的判据覆盖率不全、判据编写门槛高、耗时多的问题日益凸显，缺少较通用的算法对传统判读算法未覆盖的判读任务进行判读补充，进而影响运载火箭效果评估与系统性能评定。为充分挖掘海量遥测数据中隐含的参数变化规律，设计智能判读算法作为传统算法的有益补充，提升传统判读的判读覆盖率和判读效率。以液体运载火箭长期加电试验产生的遥测数据为研究对象，设计集成神经网络智能判读算法，在给出的判读指标下研究得出，集成神经网络在频率异常、丢帧等五种现有判据难以描述的判读场景下，判读性能提升30%，提高了现有判据的覆盖率，后续可为判读体系完善和智能判读落地提供研究参考。     

Source free unsupervised domain adaptation for electro-mechanical actuator fault diagnosis

A common necessity for prior unsupervised domain adaptation methods that can improve the domain adaptation in unlabeled target domain dataset is access to source domain dataset and target domain dataset simultaneously. However, data privacy makes it not always possible to access source domain dataset and target domain dataset in actual industrial equipment simultaneously, especially for aviation component like Electro-Mechanical Actuator (EMA) whose dataset are often not shareable due to the data copyright and confidentiality. To address this problem, this paper proposes a source free unsupervised domain adaptation framework for EMA fault diagnosis. The proposed framework is a combination of feature network and classifier. Firstly, source domain datasets are only applied to train a source model. Secondly, the well-trained source model is transferred to target domain and classifier is frozen based on source domain hypothesis. Thirdly, nearest centroid filtering is introduced to filter the reliable pseudo labels for unlabeled target domain dataset, and finally, supervised learning and pseudo label clustering are applied to fine-tune the transferred model. In comparison with several traditional unsupervised domain adaptation methods, case studies based on low- and high-frequency monitoring signals on EMA indicate the effectiveness of the proposed method.     

Energy-efficient data collection for UAV-assisted IoT: Joint trajectory and resource optimization

Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. As energy-efficient operations are critical to prolong the lifetime of the energy-constrained IoT devices, the Unmanned Aerial Vehicle (UAV) can be dispatched to geographically approach the IoT clusters towards energy-efficient IoT transmissions. This paper intends to maximize the system energy efficiency by considering both the IoT transmission energy and UAV propulsion energy, where the UAV trajectory and IoT communication resources are jointly optimized. By applying large-system analysis and Dinkelbach method, the original fractional optimization is approximated and reformulated in the form of subtraction, and further a block coordinate descent framework is employed to update the UAV trajectory and IoT communication resources iteratively. Extensive simulation results are provided to corroborate the effectiveness of the proposed method.     

MC-LRF based pose measurement system for shipborne aircraft automatic landing

Due to the portability and anti-interference ability, vision-based shipborne aircraft automatic landing systems have attracted the attention of researchers. In this paper, a Monocular Camera and Laser Range Finder (MC-LRF)-based pose measurement system is designed for shipborne aircraft automatic landing. First, the system represents the target ship using a set of sparse landmarks, and a two-stage model is adopted to detect landmarks on the target ship. The rough 6D pose is measured by solving a Perspective-n-Point problem. Then, once the rough pose is measured, a region-based pose refinement is used to continuously track the 6D pose in the subsequent image sequences. To address the low accuracy of monocular pose measurement in the depth direction, the designed system adopts a laser range finder to obtain an accurate range value. The measured rough pose is iteratively optimized using the accurate range measurement. Experimental results on synthetic and real images show that the system achieves robust and precise pose measurement of the target ship during automatic landing. The measurement means error is within 0.4° in rotation, and 0.2% in translation, meeting the requirements for automatic fixed-wing aircraft landing.Received 5 July 2022; revised 19 August 2022; accepted 27 September 2022.     

基于XTCE标准的遥测参数装订与核对研究

面对我国现代航天高密度发射的需求，精准高效的遥测参数装订与核对尤为重要。本文基于XTCE标准定义的遥测参数格式，构建了XTCE标准对遥测参数数据底座，实现遥测参数的统一标准、统一管理和统一平台。阐述了XTCE标准的结构、火箭遥测数据库的生成和数据底座的构造。实现了对大型火箭遥测参数的自动装订，大幅提高了工作效率。     

一种靶场试验遥测数据处理与分析系统的设计与实现

针对靶场试验遥测数据特点,设计了一种遥测数据处理分析系统。作为靶场测试流程和飞行结果判别的重要环节,遥测数据处理分析系统软件实现了繁杂测试数据处理与发布、数据管理和数据判读与服务等功能。该系统的设计与实现为靶场数字化建设提供了重要支撑,具有较强的实用性与推广价值。     

Contributions to data transmission at critical moments on unmanned aerial vehicles

As for unmanned aircraft, the knowledge of the aircraft performance is directly related with the navigation, guidance, and control system programming. Therefore, the measured data in each phase of the flight must be sufficiently precise to obtain a good characterization of aircraft. This article proposes new methods of sending information to ground, which make it possible to know the aircraft behavior accurately, and for this purpose, four contributions have been made for ALO (Avión Ligero de Observación, Spanish acronym for Light Observation Aircraft). Currently, the characterization is based on data obtained at ten samples per second, insufficient to acquire detailed knowledge of what happened during the whole flight of an aircraft. As a result of these contributions, many more samples per second of accelerations and angular velocities are obtained at the most critical moments of the flight, such as takeoff or landing. Among the improvements included are data compression techniques, providing references to locate the measured data in time and identifying labels of each parameter.     

Adaptive data fusion framework for modeling of non-uniform aerodynamic data

Multi-fidelity Data Fusion(MDF) frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels. However, most existing MDF frameworks assume a uniform data structure between sampling data sources; thus, producing an accurate solution at the required level, for cases of non-uniform data structures is challenging. To address this challenge, an Adaptive Multi-fidelity Data Fusion(...     

基于时空数据模型的障碍物数据集数据查询与应用研究

障碍物数据集是国际民航组织在航空情报管理（AIM）体系下提出的一种基于航空信息交换模型（AIXM）规范的航空情报数据集，该数据集中障碍物的查询将直接影响机场净空评估与飞行程序设计。在分析障碍物数据集的时间与空间属性基础上，利用时空数据模型与航空信息交换模型规范，提出一种基于时间点和空间位置的障碍物数据集查询方法， 用于解决障碍物数据集的查询问题；构建障碍物查询与可视化系统，并通过设计随机实验和实例应用对该方法的可行性和可靠性进行验证。结果表明：该障碍物数据集查询方法能够提取出对机场净空与飞行程序设计有影响的障碍物，增强了飞行程序设计人员对机场障碍物分布的情景意识。