As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current s...As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.展开更多
Due to the inherent nature of being highly digitalized,networked and intelligent,Unmanned Aerial System(UAS)operations pose a huge challenge to traditional aviation regulation and technical systems.How to keep safe,ef...Due to the inherent nature of being highly digitalized,networked and intelligent,Unmanned Aerial System(UAS)operations pose a huge challenge to traditional aviation regulation and technical systems.How to keep safe,efficient and integrated operation for different Airspace users has become a pressing issue faced by civil aviation around the world.This paper focuses on the main operational scenarios and characteristics of unmanned aviation development in China.New operational characteristics and associated challenges due to diverse low-altitude users are analyzed,including operation concepts,UAS traffic management,technological test and verification,and standards.Drawing light on the practices in Europe and the United States,this paper summarizes China's practices and progress in low-altitude operations management,and analyzes future technological development needs and trends,as well as feasible implementation pathways and measures based on actual needs.展开更多
Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movemen...Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movement and some other conditions.We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner,especially aiming at unmanned aerial vehicle(UAV)tracking.First,it applies a dense network to reduce vanishing-gradient,which strengthens the features transfer.Second,the channel attention mechanism is involved into the Densenet structure,in order to focus on the possible key regions.The advance corner detection network is introduced to improve the following tracking process.Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015,UAV123,LaSOT and VOT.The accuracy rate on UAV123 is 78.9%,and the running speed is 32 frame per second(FPS),which demonstrates its efficiency in the practical real application.展开更多
The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage.To reach a balance between green transportation and competitiv...The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage.To reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily services.In this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion time.Furthermore,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality solution.The numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.展开更多
基金co-supported by the National Natural Science Foundation of China (Nos. U1933130,71731001,1433203,U1533119)the Research Project of Chinese Academy of Sciences (No. ZDRW-KT-2020-21-2)。
文摘As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.
基金This work was supported by National Natural Science Foundation of China(Grant Nos.U1933130)research and demonstration of key technologies for the air-ground collaborative and smart operation of general aviation(No.2022C01055)。
文摘Due to the inherent nature of being highly digitalized,networked and intelligent,Unmanned Aerial System(UAS)operations pose a huge challenge to traditional aviation regulation and technical systems.How to keep safe,efficient and integrated operation for different Airspace users has become a pressing issue faced by civil aviation around the world.This paper focuses on the main operational scenarios and characteristics of unmanned aviation development in China.New operational characteristics and associated challenges due to diverse low-altitude users are analyzed,including operation concepts,UAS traffic management,technological test and verification,and standards.Drawing light on the practices in Europe and the United States,this paper summarizes China's practices and progress in low-altitude operations management,and analyzes future technological development needs and trends,as well as feasible implementation pathways and measures based on actual needs.
基金supported by the Zhejiang Key Laboratory of General Aviation Operation Technology(No.JDGA2020-7)the National Natural Science Foundation of China(No.62173237)+3 种基金the Natural Science Foundation of Liaoning Province(No.2019-MS-251)the Talent Project of Revitalization Liaoning Province(No.XLYC1907022)the Key R&D Projects of Liaoning Province(No.2020JH2/10100045)the High-Level Innovation Talent Project of Shenyang(No.RC190030).
文摘Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movement and some other conditions.We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner,especially aiming at unmanned aerial vehicle(UAV)tracking.First,it applies a dense network to reduce vanishing-gradient,which strengthens the features transfer.Second,the channel attention mechanism is involved into the Densenet structure,in order to focus on the possible key regions.The advance corner detection network is introduced to improve the following tracking process.Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015,UAV123,LaSOT and VOT.The accuracy rate on UAV123 is 78.9%,and the running speed is 32 frame per second(FPS),which demonstrates its efficiency in the practical real application.
基金supported by the National Natural Science Foundation of China(No.61961146005)。
文摘The advancement of autonomous technology makes electric-powered drones an excellent choice for flexible logistics services at the last mile delivery stage.To reach a balance between green transportation and competitive edge,the collaborative routing of drones in the air and trucks on the ground is increasingly invested in the next generation of delivery,where it is particularly reasonable to consider customer time windows and time-dependent travel times as two typical time-related factors in daily services.In this paper,we propose the Vehicle Routing Problem with Drones under Time constraints(VRPD-T)and focus on the time constraints involved in realistic scenarios during the delivery.A mixed-integer linear programming model has been developed to minimize the total delivery completion time.Furthermore,to overcome the limitations of standard solvers in handling large-scale complex issues,a space-time hybrid heuristic-based algorithm has been developed to effectively identify a high-quality solution.The numerical results produced from randomly generated instances demonstrate the effectiveness of the proposed algorithm.
