This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-gu...This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.展开更多
有效的武器目标分配(weapon-target assignment,WTA)方法对减少作战损失,提高防御效果具有重要意义。针对防空资源分配问题建立合理的数学模型,以最大化目标毁伤效能和最小化雷达资源消耗为优化目标,同时考虑雷达通道数上限等多个约束,...有效的武器目标分配(weapon-target assignment,WTA)方法对减少作战损失,提高防御效果具有重要意义。针对防空资源分配问题建立合理的数学模型,以最大化目标毁伤效能和最小化雷达资源消耗为优化目标,同时考虑雷达通道数上限等多个约束,在基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)基础上进行改进,种群进化过程中自适应调整交叉与变异的概率以提高个体的质量,最终得到一组可供决策者使用的最优解集。实验结果表明:与其他多目标进化算法相比,该算法能得到适应度更高且分布性良好的结果,能够为防空导弹武器目标分配问题提供可行方案。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
文摘This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.
文摘有效的武器目标分配(weapon-target assignment,WTA)方法对减少作战损失,提高防御效果具有重要意义。针对防空资源分配问题建立合理的数学模型,以最大化目标毁伤效能和最小化雷达资源消耗为优化目标,同时考虑雷达通道数上限等多个约束,在基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)基础上进行改进,种群进化过程中自适应调整交叉与变异的概率以提高个体的质量,最终得到一组可供决策者使用的最优解集。实验结果表明:与其他多目标进化算法相比,该算法能得到适应度更高且分布性良好的结果,能够为防空导弹武器目标分配问题提供可行方案。
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。
