Unmanned aerial vehicles (UAVs) have gradually become a major air threat to ships because of small size, good maneuverability, and low cost. Vision-based UAV detection offers one of the main ways to identify and protect against UAVs. Unlike land environment, the weather is complicated at sea. The visibility of an object is undermined by such factors as sea fog and sunlight, which makes it difficult to detect UAVs at sea through visionbased object detection. For the purpose of object detection at sea, this paper proposes a UAV object detection method based on image haze removal. In the proposed method, an improved dark channel haze removal (DCHR) algorithm is utilized to remove haze for and restore video images. Additionally, co-ordinate attention (CoordAttention, CA) is introduced to the lightweight algorithms of You Only Look Once (YOLO) for the object detection in restored video images, so as to improve the precision and speed of detection and reduce the miss rate. Some video images are also taken for detection experiments to verify the feasibility and effectiveness of the proposed method.
To integrating of combat simulation system in existence, the barrier of differences of runtime framework, connection protocols and integration framework among the simulation systems must be solved. HLA bridge is necessary when the different RTI implements, which decline the system performance. The Data Distribution Service (DDS), which is implemented with P/S mode, with the advantage of low coupled, strong scalability real-time and huge amount, seems an ideal solution to the problem. An integration framework based on DDS is approached, which inherits the real-time communication ability of DDS and the time-management and federation management ability of HLA, the prototype system proved the ability of the framework, and the semantics communication module of the system would be the next research topic.
The aim of this paper is to present a novel sliding mode control scheme for the supercavitating vehicle trajectory tracking problem that subjects to external disturbances and actuator saturation with two symmetric elevators and a cavitator as actuators by analytical methods and computer simulations. Firstly, the nonlinear and highly coupled dynamic and kinematic models of a supercavitating vehicle are presented in a comprehensive way by taking the cavity memory effect and time-variant planing force into consideration. The PSO algorithm is employed to optimize the control parameters for achieving better and more practical tracking performance by minimizing the objective function. A second-order extended state observer (ESO) is utilized to estimate the unknown external and state-dependent disturbances and compensates for control inputs. In addition, an antiwindup compensator is adopted to cope with actuator saturation. Finally, the proposed control scheme is employed for complex trajectory tracking of a supercavitating vehicle under various conditions by conducting comparative numerical simulations. Rigorous theoretical analysis and simulation results indicate that the proposed control scheme can achieve satisfactory tracking performance and have a good capability of robustness.
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