Affected by the insufficient information of single baseline observation data,the three-stage method assumes the Ground-to-Volume Ratio(GVR)to be zero so as to invert the vegetation height.However,this assumption intro...Affected by the insufficient information of single baseline observation data,the three-stage method assumes the Ground-to-Volume Ratio(GVR)to be zero so as to invert the vegetation height.However,this assumption introduces much biases into the parameter estimates which greatly limits the accuracy of the vegetation height inversion.Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR.Nevertheless,the similar model parameter values in a multi-baseline model often lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm.To this end,we propose a new step-by-step inversion method applied to the multi-baseline observations.Firstly,an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data,and the regularized estimates of model parameters are obtained by regularization method.Then,the reliable estimates of GVR are determined by the MSE(mean square error)analysis of each regularized parameter estimation.Secondly,the estimated GVR is used to extracts the pure volume coherence,and then the vegetation height parameter is inverted from the pure volume coherence by least squares estimation.The experimental results show that the new method can improve the vegetation height inversion result effectively.The inversion accuracy is improved by 26%with respect to the three-stage method and the conventional solution of multi-baseline.All of these have demonstrated the feasibility and effectiveness of the new method.展开更多
随着无人机的广泛应用,无人机控制器的设计成为近年来广泛研究的热点。当前无人机中广泛使用的PID,MPC等控制算法受到参数难调节、模型构建复杂、计算量大等一系列因素的制约。针对上述问题,提出了一种基于深度强化学习的无人机自主控...随着无人机的广泛应用,无人机控制器的设计成为近年来广泛研究的热点。当前无人机中广泛使用的PID,MPC等控制算法受到参数难调节、模型构建复杂、计算量大等一系列因素的制约。针对上述问题,提出了一种基于深度强化学习的无人机自主控制方法。该方法通过神经网络拟合无人机控制器,直接将无人机的状态映射到舵机的输出以控制无人机运动,在不断与环境进行交互训练中即可得到一个通用的无人机控制器,有效地避免了参数调节、模型构建等复杂操作。同时,为进一步提高模型的收敛速度和准确性,在传统强化学习算法Soft Actor Critic(SAC)的基础之上引入专家信息,提出了ESAC算法,指导无人机对环境进行探索,以增强控制策略的易用性和扩展性。最后在无人机的位置控制以及轨迹跟踪任务中,通过与传统PID控制器和SAC,DDPG等强化学习算法构建的模型控制器进行对比,实验结果表明,通过ESAC算法构建的控制器能够达到与PID控制器同样甚至更优的控制效果,同时在稳定性和准确性上优于SAC和DDPG构建的控制器。展开更多
基金National Natural Science Foundation of China(No.42104025)China Postdoctoral Science Foundation(No.2021M702509)+3 种基金Natural Resources Sciences and Technology Project of Hunan Province(No.2022-07)Surveying and Mapping Basic Research Foundation of Key Laboratory of Geospace Environment and Geodesy,Ministry of Education(No.20-01-04)Natural Science Foundation of Hunan Province(No.2024JJ5144)Open Fund of Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway(Changsha University of Science&Technology,No.kfj190805).
文摘Affected by the insufficient information of single baseline observation data,the three-stage method assumes the Ground-to-Volume Ratio(GVR)to be zero so as to invert the vegetation height.However,this assumption introduces much biases into the parameter estimates which greatly limits the accuracy of the vegetation height inversion.Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR.Nevertheless,the similar model parameter values in a multi-baseline model often lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm.To this end,we propose a new step-by-step inversion method applied to the multi-baseline observations.Firstly,an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data,and the regularized estimates of model parameters are obtained by regularization method.Then,the reliable estimates of GVR are determined by the MSE(mean square error)analysis of each regularized parameter estimation.Secondly,the estimated GVR is used to extracts the pure volume coherence,and then the vegetation height parameter is inverted from the pure volume coherence by least squares estimation.The experimental results show that the new method can improve the vegetation height inversion result effectively.The inversion accuracy is improved by 26%with respect to the three-stage method and the conventional solution of multi-baseline.All of these have demonstrated the feasibility and effectiveness of the new method.
文摘随着无人机的广泛应用,无人机控制器的设计成为近年来广泛研究的热点。当前无人机中广泛使用的PID,MPC等控制算法受到参数难调节、模型构建复杂、计算量大等一系列因素的制约。针对上述问题,提出了一种基于深度强化学习的无人机自主控制方法。该方法通过神经网络拟合无人机控制器,直接将无人机的状态映射到舵机的输出以控制无人机运动,在不断与环境进行交互训练中即可得到一个通用的无人机控制器,有效地避免了参数调节、模型构建等复杂操作。同时,为进一步提高模型的收敛速度和准确性,在传统强化学习算法Soft Actor Critic(SAC)的基础之上引入专家信息,提出了ESAC算法,指导无人机对环境进行探索,以增强控制策略的易用性和扩展性。最后在无人机的位置控制以及轨迹跟踪任务中,通过与传统PID控制器和SAC,DDPG等强化学习算法构建的模型控制器进行对比,实验结果表明,通过ESAC算法构建的控制器能够达到与PID控制器同样甚至更优的控制效果,同时在稳定性和准确性上优于SAC和DDPG构建的控制器。
