无人机集群组网高精度测距技术研究

针对无人机组网系统机间测距精度受限于时域采样周期时延估计的问题，本文提出一种联合正交频分复用（OFDM）前导序列和导频序列进行高精度测距估计的方法，利用最大似然估计和双向测距法将参与计算的序列测距估计结果按比例进行加权平均。同时，仿真分析了联合测距估计方法性能。结果表明：联合估计方法的前导与导频序列差分自相关间隔长度取典型值时测距精度能达到0.1 m以下，可在较低信噪比下实现厘米级测距精度，提升了集群组网测距的稳定性。     

面向自动驾驶仿真的虚拟LiDAR传感器建模

在自动驾驶仿真领域，虚拟传感器输出数据的精准度是仿真结果可靠性的重要保障。激光雷达（LiDAR）作为车辆环境感知的关键传感器，其采集的点云数据的准确性是实现车辆对三维环境理解的关键。但在虚拟环境中，通过3D渲染技术模拟的点云数据难以真实反映传感器在复杂工况下的变化规律。本文提出一种用于自动驾驶仿真的虚拟LiDAR传感器建模方法。该方法首先基于Unity 3D引擎构建LiDAR的几何测量模型。其次，结合真实传感器的衰变特性推导简化的LiDAR物理模型。最后，基于蒙特卡罗方法在随机模型上对仿真数据进行噪声模拟，从而实现高保真的LiDAR数据输出。所提出的方法可结合精细化的虚拟场景进行数据验证，实验结果表明：该方法能够有效地在虚拟环境下模拟LiDAR数据，从而应用于自动驾驶仿真算法验证过程。     

Multi-scale simulation model of air system based on cross-dimensional data transmission method

The Secondary Air System (SAS) plays an important role in the safe operation and performance of aeroengines. The traditional 1D-3D coupling method loses information when used for secondary air systems, which affects the calculation accuracy. In this paper, a Cross-dimensional Data Transmission method (CDT) from 3D to 1D is proposed by introducing flow field uniformity into the data transmission. First, a uniformity index was established to quantify the flow field parameter distribution characteristics, and a uniformity index prediction model based on the locally weighted regression method (Lowess) was established to quickly obtain the flow field information. Then, an information selection criterion in 3D to 1D data transmission was established based on the Spearman rank correlation coefficient between the uniformity index and the accuracy of coupling calculation, and the calculation method was automatically determined according to the established criterion. Finally, a modified function was obtained by fitting the ratio of the 3D mass-average parameters to the analytical solution, which are then used to modify the selected parameters at the 1D-3D interface. Taking a typical disk cavity air system as an example, the results show that the calculation accuracy of the CDT method is greatly improved by a relative 53.88% compared with the traditional 1D-3D coupling method. Furthermore, the CDT method achieves a speedup of 2 to 3 orders of magnitude compared to the 3D calculation.     

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。     

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(...