针对城市峡谷及高动态应用场景下卫星伪距观测受大气建模残差、多径及观测噪声影响,易产生随机扰动与缓慢变化的偏差,从而导致伪距误差增大并呈现明显的非平稳特征的问题,提出了一种移动射频测距辅助的卫星伪距误差抑制方法.该方法在原...针对城市峡谷及高动态应用场景下卫星伪距观测受大气建模残差、多径及观测噪声影响,易产生随机扰动与缓慢变化的偏差,从而导致伪距误差增大并呈现明显的非平稳特征的问题,提出了一种移动射频测距辅助的卫星伪距误差抑制方法.该方法在原始北斗卫星伪距观测基础上,引入具有独立量测特性的移动射频测距信息,构建对卫星伪距的补充观测约束,利用两者在误差来源上的独立性,在联合解算过程中抑制伪距误差的传播.无人机动态实验结果表明,所提出的方法能够显著降低北斗卫星伪距残差的均方根(root mean square,RMS),相较于载波相位平滑伪距方法,伪距残差RMS降低82.3%.所提出的移动射频测距辅助卫星伪距误差抑制方法在复杂动态与遮挡环境下具有良好的伪距误差抑制能力,克服了载波相位平滑伪距对连续载波锁定的依赖及其对系统性偏差抑制能力不足的局限,具有鲁棒性强、适应性好的特点,为复杂环境下北斗卫星伪距误差抑制与观测质量提升提供了一种有效途径.展开更多
Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressin...Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.展开更多
文摘针对城市峡谷及高动态应用场景下卫星伪距观测受大气建模残差、多径及观测噪声影响,易产生随机扰动与缓慢变化的偏差,从而导致伪距误差增大并呈现明显的非平稳特征的问题,提出了一种移动射频测距辅助的卫星伪距误差抑制方法.该方法在原始北斗卫星伪距观测基础上,引入具有独立量测特性的移动射频测距信息,构建对卫星伪距的补充观测约束,利用两者在误差来源上的独立性,在联合解算过程中抑制伪距误差的传播.无人机动态实验结果表明,所提出的方法能够显著降低北斗卫星伪距残差的均方根(root mean square,RMS),相较于载波相位平滑伪距方法,伪距残差RMS降低82.3%.所提出的移动射频测距辅助卫星伪距误差抑制方法在复杂动态与遮挡环境下具有良好的伪距误差抑制能力,克服了载波相位平滑伪距对连续载波锁定的依赖及其对系统性偏差抑制能力不足的局限,具有鲁棒性强、适应性好的特点,为复杂环境下北斗卫星伪距误差抑制与观测质量提升提供了一种有效途径.
基金the Collaborative Innovation Project of Shanghai,China for the financial support。
文摘Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.
