This paper studies the problem of tracking a ground target for a fixed-wing unmanned aerial vehicle(UAV) based on the proposed guidance law. The algorithm ensures that a UAV continuously overflies the target whether i...This paper studies the problem of tracking a ground target for a fixed-wing unmanned aerial vehicle(UAV) based on the proposed guidance law. The algorithm ensures that a UAV continuously overflies the target whether it is fixed or moving. The requirements of the UAV flight constraints such as bounded airspeed and acceleration are considered. A Lyapunov function is constructed to prove the stability of the proposed guidance law,and parameter design criteria have been developed. Considering the fixed and moving ground targets, numerical simulations are performed to verify the feasibility and benefits of the proposed guidance algorithm.展开更多
A Target State Estimator (TSE) for airborne radar system is proposed in this paper. It is very important for fire control system to obtain accurate estimation of the maneuvering target and the TSE becomes a key link i...A Target State Estimator (TSE) for airborne radar system is proposed in this paper. It is very important for fire control system to obtain accurate estimation of the maneuvering target and the TSE becomes a key link in the integrated Flight/Fire Control (IFFC) system. By adopting the Cartesian coordinates and pseudomeasurements ,the result ed TSE has it s advantages in computation.In addition, by employing accurate range and range-rate redundant filter, the range direction estimations obtained in Cartesian filter are greatly improved. The TSE shows its satisfaCtory performance in the Monte Carlo simulation of the IFFC system.展开更多
针对低信噪比场景下多飞行目标波达方向(Direction of Arrival,DOA)估计精度不高,导致基于智能天线的民航地空通信抗干扰性能较差的问题,提出了一种基于航向训练模式和动态径向基神经网络(Radial Basis Function Neural Network,RBFNN)...针对低信噪比场景下多飞行目标波达方向(Direction of Arrival,DOA)估计精度不高,导致基于智能天线的民航地空通信抗干扰性能较差的问题,提出了一种基于航向训练模式和动态径向基神经网络(Radial Basis Function Neural Network,RBFNN)的多飞行目标追踪方法。首先融合二次雷达信息,建立民航飞行目标DOA变换关系;然后通过航向训练模式,粗估下一时刻各飞行目标DOA,并作为RBFNN的输入;最后构建隐含层中心动态调整的RBFNN,快速准确追踪各飞行目标DOA。实验表明,该方法可以大幅提高空中同时存在的多飞行目标DOA估计精度;结合波束形成技术,可以大幅提高民航地空通信系统的抗干扰能力,提升民航飞行安全水平;在5 dB信噪比条件下,相对基于常规智能天线的民航地空通信系统,抗干扰能力可以提升16 dB。展开更多
基金supported by the Aeronautical Science Foundation of China(20160152001)
文摘This paper studies the problem of tracking a ground target for a fixed-wing unmanned aerial vehicle(UAV) based on the proposed guidance law. The algorithm ensures that a UAV continuously overflies the target whether it is fixed or moving. The requirements of the UAV flight constraints such as bounded airspeed and acceleration are considered. A Lyapunov function is constructed to prove the stability of the proposed guidance law,and parameter design criteria have been developed. Considering the fixed and moving ground targets, numerical simulations are performed to verify the feasibility and benefits of the proposed guidance algorithm.
文摘A Target State Estimator (TSE) for airborne radar system is proposed in this paper. It is very important for fire control system to obtain accurate estimation of the maneuvering target and the TSE becomes a key link in the integrated Flight/Fire Control (IFFC) system. By adopting the Cartesian coordinates and pseudomeasurements ,the result ed TSE has it s advantages in computation.In addition, by employing accurate range and range-rate redundant filter, the range direction estimations obtained in Cartesian filter are greatly improved. The TSE shows its satisfaCtory performance in the Monte Carlo simulation of the IFFC system.
文摘针对低信噪比场景下多飞行目标波达方向(Direction of Arrival,DOA)估计精度不高,导致基于智能天线的民航地空通信抗干扰性能较差的问题,提出了一种基于航向训练模式和动态径向基神经网络(Radial Basis Function Neural Network,RBFNN)的多飞行目标追踪方法。首先融合二次雷达信息,建立民航飞行目标DOA变换关系;然后通过航向训练模式,粗估下一时刻各飞行目标DOA,并作为RBFNN的输入;最后构建隐含层中心动态调整的RBFNN,快速准确追踪各飞行目标DOA。实验表明,该方法可以大幅提高空中同时存在的多飞行目标DOA估计精度;结合波束形成技术,可以大幅提高民航地空通信系统的抗干扰能力,提升民航飞行安全水平;在5 dB信噪比条件下,相对基于常规智能天线的民航地空通信系统,抗干扰能力可以提升16 dB。
