针对航天器与非合作目标追逃博弈的生存型微分对策拦截问题,基于强化学习研究了追逃博弈策略,提出了自适应增强随机搜索(adaptive-augmented random search,A-ARS)算法。针对序贯决策的稀疏奖励难题,设计了基于策略参数空间扰动的探索方...针对航天器与非合作目标追逃博弈的生存型微分对策拦截问题,基于强化学习研究了追逃博弈策略,提出了自适应增强随机搜索(adaptive-augmented random search,A-ARS)算法。针对序贯决策的稀疏奖励难题,设计了基于策略参数空间扰动的探索方法,加快策略收敛速度;针对可能过早陷入局部最优问题设计了新颖度函数并引导策略更新,可提升数据利用效率;通过数值仿真验证并与增强随机搜索(augmented random search,ARS)、近端策略优化算法(proximal policy optimization,PPO)以及深度确定性策略梯度下降算法(deep deterministic policy gradient,DDPG)进行对比,验证了此方法的有效性和先进性。展开更多
Sparse representation has recently been proved to be a powerful tool in image processing and object recognition.This paper proposes a novel small target detection algorithm based on this technique.By modelling a small...Sparse representation has recently been proved to be a powerful tool in image processing and object recognition.This paper proposes a novel small target detection algorithm based on this technique.By modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem,the proposed apporach successfully improves and optimizes the small target representation with innovation.Furthermore,the sparsity concentration index（SCI） is creatively employed to evaluate the coefficients of each block representation and simpfy target identification.In the detection frame,target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model（GIM）,and then sparse model solvers are applied to finding sparse representation for each sub-image block.Finally,SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target position.The effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.展开更多
研究多车辆多目标追逐的路径规划问题。提出两个基于混合整数线性规划(Mixed integer linear programming,MILP)的多目标追逐(Multi-target pursuit,MTP)模型:就近追逐和'一对一'使能追逐。在两个MIP追逐模型中,小车运动的状态...研究多车辆多目标追逐的路径规划问题。提出两个基于混合整数线性规划(Mixed integer linear programming,MILP)的多目标追逐(Multi-target pursuit,MTP)模型:就近追逐和'一对一'使能追逐。在两个MIP追逐模型中,小车运动的状态方程考虑为具有线性阻尼的质点动力学方程。采用整数变量描述小车与障碍物的相对位置信息,提出'目标膨胀尺寸'的概念来描述对目标的追逐,定义小车的'追逐方向'。采用选取整变量的等高面法求解MILP追逐问题,并给出初始内点整变量的确定方法。最后给出仿真试验1对两个多目标追逐模型进行对比研究,仿真试验2证实了算法的效率。展开更多
文摘针对航天器与非合作目标追逃博弈的生存型微分对策拦截问题,基于强化学习研究了追逃博弈策略,提出了自适应增强随机搜索(adaptive-augmented random search,A-ARS)算法。针对序贯决策的稀疏奖励难题,设计了基于策略参数空间扰动的探索方法,加快策略收敛速度;针对可能过早陷入局部最优问题设计了新颖度函数并引导策略更新,可提升数据利用效率;通过数值仿真验证并与增强随机搜索(augmented random search,ARS)、近端策略优化算法(proximal policy optimization,PPO)以及深度确定性策略梯度下降算法(deep deterministic policy gradient,DDPG)进行对比,验证了此方法的有效性和先进性。
基金supported by the Inter-governmental Science and Technology Cooperation Project (2009DFA12870)
文摘Sparse representation has recently been proved to be a powerful tool in image processing and object recognition.This paper proposes a novel small target detection algorithm based on this technique.By modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem,the proposed apporach successfully improves and optimizes the small target representation with innovation.Furthermore,the sparsity concentration index（SCI） is creatively employed to evaluate the coefficients of each block representation and simpfy target identification.In the detection frame,target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model（GIM）,and then sparse model solvers are applied to finding sparse representation for each sub-image block.Finally,SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target position.The effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.
文摘研究多车辆多目标追逐的路径规划问题。提出两个基于混合整数线性规划(Mixed integer linear programming,MILP)的多目标追逐(Multi-target pursuit,MTP)模型:就近追逐和'一对一'使能追逐。在两个MIP追逐模型中,小车运动的状态方程考虑为具有线性阻尼的质点动力学方程。采用整数变量描述小车与障碍物的相对位置信息,提出'目标膨胀尺寸'的概念来描述对目标的追逐,定义小车的'追逐方向'。采用选取整变量的等高面法求解MILP追逐问题,并给出初始内点整变量的确定方法。最后给出仿真试验1对两个多目标追逐模型进行对比研究,仿真试验2证实了算法的效率。
