With the advancement of UAV technology, it is imperative to deliberate and practice the method of UAV operation management in greater detail. This paper presents a design scheme for an urban air traffic management data processing system based on a digital twin, which can solve the problem of multi-operator system digital management for conflict warning management. The system is composed of a digital core module, a digital control module, and a UAV twin flight module. The modules are linked to each other, and utilizing techniques like flight planning, comprehensive warning, and space processing, the twin-algorithm data analysis framework is constructed, and conflict predictions and predictions are made. The process of confirming the system described in this paper will be followed by further refinements and, with the assistance of the Bureau, the collection of information about the sector's activities to enhance the system.

This work presents a minimum separation calculation for the integrated operation of manned and unmanned aerial vehicles in an uncertain airspace environment. Different from traditional path-planning-based research, this study investigated the minimum safe separation distance from a novel perspective of reachability analysis. The proposed computational method made use of the Hamilton–Jacobi partial differential equation (HJPDE) to obtain the backward reachable tube. Firstly, this work modeled the integrated operation in the UAS traffic management scenario, particularly focusing on the uncertainties. Then, a probabilistic reachability tube computation method was derived. Next, this work calculated the safe separation distances based on reachability analysis for three scenarios: a deterministic environment, an environment with relative position uncertainty, and an environment with relative heading angle uncertainty. By calculating the reachable tubes for a given response time, the worst-case minimum safe distances from the UAV’s perspective were determined, and the quantitative patterns were summarized. The results in this work indicate that, with an increase in the risk level and under the premise of a 1 s response time, the minimum safe separation increases from 26.7 m to 30.0 m. Finally, the paper discusses the results, explaining their rationality from both mathematical and physical perspectives.

This paper focuses primarily on urban air mobility (UAM), including air transportation for passengers and goods in metropolitan areas. Simulations of UAM in urban environment are conducted. Firstly, quadrotor configuration is determined as the research object of this paper. Based on the flight dynamic equations, the 6-DOF mathematical model of quad-rotor UAS is established. A flight control law based on nonlinear dynamic inversion method is designed for the quadrotor to follow the expected path. Then, the airspace below 120 m is divided into several layers. UAM aircrafts with a similar flight heading fly in a same layer and the vertical separation of high risk UAM aircrafts can be maintained. The flight profile of UAM aircraft is designed based on the layered airspace, including the flight attitude and heading in the flight phases. Finally, to verify the concept proposed in this paper, several mathematic simulations are conducted based on the urban airspace environment described above. The simulations include complete flight processes of multi-UAM in layered airspace. The simulation data is collected and results are analyzed. It is concluded that with proper management of airspace and flight control algorithm, this work is able to simulate the UAM operation in urban environment. The proposed airspace management method could be effective to guarantee the flight safety of UAM. At last of this paper, the research conclusion and expected future work are discussed.

今年4月,《交通领域科技创新中长期发展规划纲要(2021—2035年)》正式发布,明确将飞行汽车作为新型载运工具,部署超前研发、突破关键技术.一石激起千层浪,曾经只在电影和漫画中的想象,即将走入公众生活.但回顾飞行汽车百年探索历程,梦想与现实之间,缺的是现实应用场景.

This paper solves the flight control law design problem of a fixed wing unmanned aerial vehicle(UAV) in aerobatic maneuvers. First, a six degree-of-freedom nonlinear dynamic model was established as the research object. Then, based on the nonlinear dynamic inversion control approach, a height-flight path angle-velocity configuration, a pitch rate-roll rate-throttle control law and an angle of attack-roll rate-throttle configuration were proposed. The structures of flight control law were derived and the mathematical models were established. Then, the control inputs of each phase were calculated based on the kinematic characteristics of two selected maneuvers: S-turn and Split S maneuvers. At last, the complete closed-loop simulations were conducted and the results indicates that the proposed control law configurations as well as the maneuver analysis method were able to maintain the attitudes and trajectories accurately.

The small Unmanned Aircraft Systems (sUAS) with Beyond Visual Line of Sight (BVLOS) have already been used for commercial operation in urban, which triggered the new operation risk. At present, the Specific Operation Risk Assessment (SORA) method has widely adopt by the competent authorization, but the acceptable means of compliance (AMC) are not sufficient to support the implementation of SORA. Overall, the determination of operational volume (OV) is one of the most urgent problems. In this paper, we firstly supposed the OV geography and summarized the possible operation scenarios. Then the determination method was proposed based on the key influencing factors. Finally, an urban logistical operation case with large amount of practical data was selected to verify the method. The results were analyzed and demonstrated the method was available.

无人驾驶航空器及其衍生系统(以下简称无人机)是21世纪航空业发展的最大技术变量.中美欧都具备相对完整的民用无人机产业链,具备主导全球民用无人机发展的实力与动力,也是全球无人机产业主要的创新源与目标市场.美欧在社会动员、无人机融入空域战略、运行技术研发方面不遗余力,值得我国关注并自主推进无人机融入空域战略.

: With the rapid development of civilian unmanned aircraft system (UAS) in the fields of logistics and distribution, geographic information detection, and emergency rescue, the U.S. Federal Aviation Administration (FAA) and the National Aeronautics and Space Administration (NASA) have jointly developed the Unmanned Aircraft System Traffic Management System (UTM) and carried out a large number of verification tests. According to the technical level, NASA divides the operation technologies and related flight demonstration tests into four technical level phases, of which the TCL-3 and TCL-4 are the core phases of the UTM test and also the most technically complex phases. This article summarized the third and fourth phases of the flight demonstration tests of the UTM system in the United States. Based on the key technologies, the test contents and operation scenarios, as well as the relevant flight experience were summarized. Finally, some recommendations for UTM system design in China were put forward.

无人机是基于算力平台的新一代航空技术,与基于动力平台的传统航空器相比,具有机械简单、数据丰富、智能化潜力足等特点,无人机充分借助了数字传感器、数据链、物联网等数字化信息化等指数式增长技术的动力,具备助推民航产业链升级换代的潜力. 民用无人机有一条很长但清晰可见的技术成熟坡道.轻小型无人机应用于航拍、空中表演、农业植保、末端物流配送,突破传统航空业社会普及的技术瓶颈而进入公众日常生活.中大型无人机将从安全性、成本、效率与长航时等方面增强通用航空作业能力.支线物流无人机、城市空中交通载客已处于商业应用的前期阶段,未来将大幅扩展民航业服务范围.大型货运无人机与可选驾驶运输机预测将在2050年左右投入规模化运行.到本世纪后半叶,无人机的智能化将与有人航空的自动化实现技术融合,并最终实现人工智能在航空业的深度应用.

民用无人机的广泛应用给国家公共安全、飞行安全监管带来了严峻挑战,越来越多的国家开始制定民用无人机运行管理法律法规.如何使民用无人机相关法律法规与技术发展、市场需求及社会认知相适应,成为各个国家面临的难题.研究了主要国际组织与国家的民用无人机运行管理框架,分析了各国法律法规的相似性和异质性,探讨了影响法律法规制定的主要因素,提出了民用无人机运行管理法律法规的制定建议.

In the framework of free flight, there can be no intersection between the protected areas of two planes. The traditional conflict resolution methods allow generally the aircraft to change the speed alone or to change angle alone. Starting from the method of linear programming and based on solving some special problems, this article presents a method that can change the speed and angle of aircraft in the same time. Finally, some tests verify the feasibility of the method.

In order to ensure flight safety, aviation industry has put forward various methods. Reasonable flight conflict resolution method is the key technology to ensure the safe flight of aircrafts. However, the traditional conflict resolution method based on linear programming only allows the aircraft to change speed or angle, which reduces the flexibility of aircraft and is difficult to meet the need of eliminating conflicts to the greatest extent. Firstly, aiming at minimizing the cost of conflict resolution, this paper constructs a mathematical model based on integer programming, which can optimize the speed, heading angle and altitude of an aircraft at the same time, and uses CPLEX software to solve the problem. Experiments in classical scenarios show that the proposed method can effectively solve the conflict resolution problem among a larger scale of aircrafts.

复杂低空空域环境下多飞行器冲突解脱方法可以有效地提供冲突解脱策略,避免飞行器之间发生危险接近事故或者碰撞,从而保障空域运行安全.目前飞行器冲突解脱方法主要可以分为集中式和分布式.然而基于人工势场法等分布式方法虽然计算速度快,但可能会产生不切实际的解;基于进化算法等集中式方法可靠性高,但是计算量大,响应速度较慢,实时性差.本文结合人工势场法与蚁群算法的优点提出改进混合冲突解脱方法,首先利用人工势场法迅速得到近似可行的冲突解脱路径,然后将方案调整、编码得到"权威蚂蚁",由"权威蚂蚁"衍生"权威蚁群",利用"权威蚁群"始化信息素矩阵,基于蚁群算法,求得含有飞行规划约束的解脱方案.并通过与传统的人工势场法与蚁群算法进行比较,验证了改进算法在时效性和可行性上的优点.

Cooperative work based on multi-UAV is the development trend in the UAV field. The work efficiency and the completion rate of tasks can be greatly improved through collaborative work of multiple UAVs. In this paper, a verification platform for UAV formation method is established, which consists of three quad-rotor UAVs and a ground control system working as a monitoring and command center. An open source UAV flight control system named Pixhawk is adopted by the quad-rotor UAVs, and the minicomputer named Raspberry Pi 3 is used to simulate the On-board computer running the formation algorithm and executing the tasks. This paper verified the feasibility and effectiveness of the UAV formation algorithm based on artificial potential field method through the verification platform established by itself.

This paper presents a possibility measure based model to detect the unstable approach in flight data analysis. The key elements of stabilized approach, energy height, and some other related flight parameters, including approach speed, touchdown speed, corrected wind speed, approach angle, landing configuration, engine thrust, and etc., are used to establish the fuzzy sets. Therefore, a fuzzy integrated judgment model is also proposed, which would quantify the distance between the current approach and the stabilized approach in the possibility space. Furthermore, the implications of this strategy for practitioners and future work are analyzed.

In this paper, a safety risk assessment algorithm is presented based on the conflict alarms of the traffic alert and collision avoidance system (TCAS). The assessment algorithm is an integrated model based on the fuzzy clustering techniques. In particular, the Fuzzy c-Means (FcM) clustering method is utilized to evaluate the hazardous severity. This assessment algorithm could be used as the safety performance indicator contributing to the construction of Safety Management System (SMS) for the airlines. The experiments show that it is effective for the safety risk assessment.

This paper presents an abnormal detection strategy to analysis the inspected parameters of the instrument landing system (ILS). Both the VHF localizer and glide path related parameters are utilized to make the comparisons with the data from other sources, such as GPS and BDS. These comparisons are designed to measure by setting up the possibility measure based model. In this model, fuzzy sets related to ILS precisions are defined. Furthermore, an integrated detection model is proposed, which would quantify the distance between the ILS and the other source data. Meanwhile the possibility measure is presented to quantify the validation of the ILS. Furthermore, the implications of this strategy for practitioners and future work are analyzed.

As the development of micro-electromechanical systems (MEMS), the cheap and small dimension MEMS IMU has been widely used in the navigation system. However, MEMS IMU presents different stochastic errors, which are difficult to be analyzed and may degrade the accuracy of the navigation systems in a short period. Considering the disadvantages of the Allan variance method in stochastic error property analysis, the indirect estimation method is proposed in this paper. By Daubechies discrete wavelet transform for the backward differential stochastic error, the statistical characteristics of wavelet coefficients is thoroughly studied. The wavelet decomposition scale is determined by wavelet coefficient statistical characteristics. Then wavelet variance is selected as the auxiliary parameter of indirect estimation, and an optimal criterion of asymptotic consistency is derived. Finally, according to the relationship between stochastic error statistical characteristics and wavelet variance, the stochastic error property parameters with asymptotic consistency are obtained by the nonlinear Gauss-Newton method. Comparing with estimated results by Allan variance method, simulation results indicates that the indirect estimation method not only improves the accuracy of parameter estimation, but also effectively resolved the issue concerning accurate parameter estimation of a first-order Markov stochastic error model.

One of the most fundamental tasks in the socially aware network (SAN) paradigm is to explore the attributes and behavior of users, which helps to design more suitable and efficient protocols. Particularly, detection of shilling attackers by mining users’ behavior is a frequently discussed topic in many social scenes like recommender systems based on collaborative filtering. As the performances of collaborative filtering are entirely based on ratings provided by users, they are vulnerable to shilling attacks which perform injection of biased profiles into rating databases to alter the systems. Current shilling attack detection methods detect spam users through artificially designed features, which are neither robust nor efficient enough. This paper illustrates a novel convolutional neural network-based method named CNN-SAD, which applies transformed network structure to exploit deep-level features from users rating profiles. Since the achieved deep-level features elaborate users rating more precisely than artificially designed features, CNN-SAD can detect shilling attacks more efficiently. According to the experimental results, the proposed method is capable of detecting the vast majority of obfuscated attacks precisely and outperforms other state-of-the-art algorithms, which contributes to applications and security in SAN.

This paper proposes an innovative deep architecture for aircraft hard landing prediction based on Quick Access Record (QAR) data. In the field of industrial IoT, the IoT devices collect IoT data and send these data to the open IoT cloud platform to process and analyze. The prediction of aircraft hard landing is one kind of typical IoT application in aviation field. Firstly, 15 most relevant landing sensor data have been chosen from 260 parameters according to the theory of both aeronautics and feature engineering. Secondly, a deep prediction model based on Long Short-Term Memory (LSTM) have been developed to predict hard landing incidents using the above-mentioned selected sensor data. And then, we adjust the model structure and conduct contrastive experiments. Finally, we use Mean Square Error (MSE) as the evaluation criteria to select the most optimal model. Experimental results prove its better performance with higher prediction accuracy on QAR datasets compared with the state-of-the-art, indicating that this model is effective and accurate for hard landing prediction, which helps to guarantee passengers’ safety and reduce the incidence of landing accidents. Besides, the proposed work is conducive to making an innovation for building and developing the industrial IoT systems in aviation field.