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 aviat...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.展开更多
In recent years,the number of incidents involved with unmanned aerial vehicles(UAVs)has increased conspicuously,resulting in an increasingly urgent demand for developing anti-UAV systems. The vast requirements of high...In recent years,the number of incidents involved with unmanned aerial vehicles(UAVs)has increased conspicuously,resulting in an increasingly urgent demand for developing anti-UAV systems. The vast requirements of high detection accuracy with respect to low altitude UAVs are put forward. In addition,the methods of UAV detection based on deep learning are of great potential in low altitude UAV detection. However,such methods need high-quality datasets to cope with the problem of high false alarm rate(FAR)and high missing alarm rate(MAR)in low altitude UAV detection,special high-quality low altitude UAV detection dataset is still lacking. A handful of known datasets for UAV detection have been rejected by their proposers for authorization and are of poor quality. In this paper,a comprehensive enhanced dataset containing UAVs and jamming objects is proposed. A large number of high-definition UAV images are obtained through real world shooting, web crawler, and data enhancement.Moreover,to cope with the challenge of low altitude UAV detection in complex backgrounds and long distance,as well as the puzzle caused by jamming objects,the noise with jamming characteristics is added to the dataset. Finally,the dataset is trained,validated,and tested by four mainstream deep learning models. The results indicate that by using data enhancement,adding noise contained jamming objects and images of UAV with complex backgrounds and long distance,the accuracy of UAV detection can be significantly improved. This work will promote the development of anti-UAV systems deeply,and more convincing evaluation criteria are provided for models optimization for UAV detection.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 genetic algorithm （GA） is a nature-inspired evolutionary algorithm to find optima in search space via the interac- tion of individuals. Recently, researchers demonstrated that the interaction topology plays an i...The genetic algorithm （GA） is a nature-inspired evolutionary algorithm to find optima in search space via the interac- tion of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topolo- gies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence.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
The interception information of infrared（ IR）-guided air-to-air missiles（ AAM） is mainly estimated only using the basic bearing measurements. In order to intercept highly maneuverable targets,it is essential to st...The interception information of infrared（ IR）-guided air-to-air missiles（ AAM） is mainly estimated only using the basic bearing measurements. In order to intercept highly maneuverable targets,it is essential to study the system observability to improve the target tracking system performance.The uniqueness of this paper is that the observability analysis is derived based on a discrete three-dimensional （3D） system model. During the maneuvering scenario,the system is approximated by a segment-by-segment system. The relationship between missile-target motion and observability is given by direct and dual approaches. Meanwhile sufficient observability conditions are derived. Moreover,a numerical simulation is conducted and an alternate method is provided to reinforce the proposed observability analysis results.展开更多
The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network ...The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm(FA) and four recently proposed FA variants.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.U1933130,U1533119 and 71731001)the Major Project of Technological Innovation,China(No.2018AAA0100800)。
文摘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.
基金supported by the National Natural Science Foundation of China(No. 62173237)the National Key R&D Program of China(No.2018AAA0100804)+7 种基金the Zhejiang Key laboratory of General Aviation Operation technology(No.JDGA2020-7)the Talent Project of Revitalization Liaoning(No. XLYC1907022)the Key R & D Projects of Liaoning Province (No. 2020JH2/10100045)the Natural Science Foundation of Liaoning Province(No. 2019-MS-251)the Scientific Research Project of Liaoning Provincial Department of Education(No.JYT2020142)the High-Level Innovation Talent Project of Shenyang (No.RC190030)the Science and Technology Project of Beijing Municipal Commission of Education (No. KM201811417005)the Academic Research Projects of Beijing Union University(No.ZB10202005)。
文摘In recent years,the number of incidents involved with unmanned aerial vehicles(UAVs)has increased conspicuously,resulting in an increasingly urgent demand for developing anti-UAV systems. The vast requirements of high detection accuracy with respect to low altitude UAVs are put forward. In addition,the methods of UAV detection based on deep learning are of great potential in low altitude UAV detection. However,such methods need high-quality datasets to cope with the problem of high false alarm rate(FAR)and high missing alarm rate(MAR)in low altitude UAV detection,special high-quality low altitude UAV detection dataset is still lacking. A handful of known datasets for UAV detection have been rejected by their proposers for authorization and are of poor quality. In this paper,a comprehensive enhanced dataset containing UAVs and jamming objects is proposed. A large number of high-definition UAV images are obtained through real world shooting, web crawler, and data enhancement.Moreover,to cope with the challenge of low altitude UAV detection in complex backgrounds and long distance,as well as the puzzle caused by jamming objects,the noise with jamming characteristics is added to the dataset. Finally,the dataset is trained,validated,and tested by four mainstream deep learning models. The results indicate that by using data enhancement,adding noise contained jamming objects and images of UAV with complex backgrounds and long distance,the accuracy of UAV detection can be significantly improved. This work will promote the development of anti-UAV systems deeply,and more convincing evaluation criteria are provided for models optimization for UAV detection.
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Project supported by the National Natural Science Foundation for Young Scientists of China(Grant No.61401011)the National Key Technologies R&D Program of China(Grant No.2015BAG15B01)the National Natural Science Foundation of China(Grant No.U1533119)
文摘The genetic algorithm （GA） is a nature-inspired evolutionary algorithm to find optima in search space via the interac- tion of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topolo- gies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence.
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 National Natural Science Foundation of China(61333011)
文摘The interception information of infrared（ IR）-guided air-to-air missiles（ AAM） is mainly estimated only using the basic bearing measurements. In order to intercept highly maneuverable targets,it is essential to study the system observability to improve the target tracking system performance.The uniqueness of this paper is that the observability analysis is derived based on a discrete three-dimensional （3D） system model. During the maneuvering scenario,the system is approximated by a segment-by-segment system. The relationship between missile-target motion and observability is given by direct and dual approaches. Meanwhile sufficient observability conditions are derived. Moreover,a numerical simulation is conducted and an alternate method is provided to reinforce the proposed observability analysis results.
基金Project supported by the National Key Laboratory of CNS/ATMBeijing Key Laboratory for Network-Based Cooperative Air Traffic Managementthe National Natural Science Foundation of China(No.71731001)
文摘The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm(FA) and four recently proposed FA variants.
