A survey of safety separation management and collision avoidance approaches of civil UAS operating in integration national airspace system

Recent years have witnessed a booming of the industry of civil Unmanned Aircraft System(UAS). As an emerging industry, the UAS industry has been attracting great attention from governments of all countries and the aviation industry. UAS are highly digitalized, informationized,and intelligent; therefore, their integration into the national airspace system has become an important trend in the development of civil aviation. However, the complexity of UAS operation poses great challenges to the traditional aviation regulatory system and technical means. How to prevent collisions between UASs and between UAS and manned aircraft to achieve safe and efficient operation in the integrated operating airspace has become a common challenge for industry and academia around the world. In recent years, the international community has carried out a great amount of work and experiments in the air traffic management of UAS and some of the key technologies.This paper attempts to make a review of the UAS separation management and key technologies in collision avoidance in the integrated airspace, mainly focusing on the current situation of UAS Traffic Management(UTM), safety separation standards, detection system, collision risk prediction, collision avoidance, safety risk assessment, etc., as well as an analysis of the bottlenecks that the current researches encountered and their development trends, so as to provide some insights and references for further research in this regard. Finally, this paper makes a further summary of some of the research highlights and challenges.     

A method for satellite time series anomaly detection based on fast-DTW and improved-KNN

In satellite anomaly detection, there are some problems such as unbalanced sample distribution, fewer fault samples, and unobvious anomaly characteristics. These problems cause the extisted anomaly detection methods are difficult to train accurate classification model, and the accuracy of anomaly detection is hard to improve. At the same time, the monitoring data of satellite has high dimension and is difficult to extract effective features. Based on the DTW over-sampling method, this paper realizes the over-sampling of fault samples in satellite time series, and constructs a distributed and balanced time series data set. The Fast-DTW method is applied to calculate the distance between different time series, which can improve the speed of similarity calculation. KNN (K-Nearest Neighbor) method is applied for classification and the best classification result is obtained by search the optimal hyper-parameters k. The results show that the proposed method has high anomaly detection accuracy and consumes short calculation time.     

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO),the network-wide 4D flight trajectories planning (N4DFTP) problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs) (3D position and time) for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategic level conflict management is developed in this paper.Specifically,a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm (MOMMA) that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the pro posed MOMMA is effective to solve the N4DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.     

DDQNC-P:A framework for civil aircraft tactical synergetic trajectory planning under adverse weather conditions

Adverse weather during aircraft operation generates more complex scenarios for tactical trajectory planning,which requires superior real-time performance and conflict-free reliability of solving methods.Multi-aircraft real-time 4D trajectory planning under adverse weather is an essen-tial problem in Air Traffic Control(ATC)and it is challenging for the existing methods to be applied effectively.A framework of Double Deep Q-value Network under the Critic guidance with heuristic Pairing(DDQNC-P)is proposed to solve this problem.An Agent for two aircraft syner-getic trajectory planning is trained by the Deep Reinforcement Learning(DRL)model of DDQNC,which completes two aircraft 4D trajectory planning tasks preliminarily under dynamic weather conditions.Then a heuristic pairing algorithm is designed to convert the multi-aircraft synergetic trajectory planning into multi-time pairwise synergetic trajectory planning,making the multi-aircraft trajectory planning problem processable for the trained Agent.This framework compresses the input dimensions of the DRL model while improving its generalization ability significantly.Sub-stantial simulations with various aircraft numbers,weather conditions,and airspace structures were conducted for performance verification and comparison.The success rate of conflict-free trajectory resolution reached 96.56％with an average calculation time of 0.41 s for 350 4D trajectory points per aircraft,finally confirming its applicability to make real-time decision-making support for con-trollers in real-world ATC systems.     

A new skeleton based flying bird detection method for low-altitude air traffic management

In low-altitude air traffic management, non-cooperation targets are the greatest threat to security of low-flying aircraft. Among various aviation fatalities, flying bird is the main factor with the highest risk and directs economic losses amounted to nearly 10 billion US dollars each year. Therefore, Flying Bird Detection (FBD) has attracted considerable attention in low-altitude air traffic management. In this paper, we propose a skeleton based FBD method via describing bird motion information with a set of key poses. To overcome the variability of birds, the skeleton feature is selected as a relatively fixed and common characteristic for the pose appearance of flying bird. Based on the geometric topology among some key parts of bird body, a set of key poses can be described by some extracted skeleton features, which are used to represent the bird motion information. Aimed at robustly handling with the pose variations, multiple pose-specific classifiers are individually trained to learn the representative poses of the flying bird. At the detection stage, the flying bird skeleton features are combined with extracted key-pose sets to perform the flying bird classification task from each image. Afterwards, the key-frame pose-change set and the consistency of the classification results from sequent images are employed to validate the final detection results. Experiments on flying bird datasets demonstrate the effectiveness and efficiency of the proposed method.     

Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control

This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle (UCAV) performing autonomous air-to-surface (A/S) attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem (RHC-OCP). In particular, an approximate elliptic launch acceptable region (LAR) model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom (2-DOF) receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.     

一种新的间断侦测器及其在DGM中的应用

根据单元交界面左右变量的差别,提出了一种新的间断侦测器构造方法。该间断侦测器的构造原理简单,编程实现容易。针对一维和二维Euler方程,我们将此间断侦测器用于间断Galerkin格式的数值计算中。数值实验表明本文构造的间断侦测器能够准确捕捉到激波的位置,从而只在间断区域引入限制器,在减少计算量、保证光滑区计算精度的同时,提高了激波等强间断的分辨率,能够明显地改善流动精细结构的模拟精度。