This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation ...This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation accuracy and robustness.We employ a manifold-based optimization approach,leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices.The proposed ESKF framework ensures state variables remain near the origin,effectively mitigating singularity issues and enhancing numerical stability.Additionally,due to the small magnitude of state variables,second-order terms can be neglected,simplifying Jacobian matrix computation and improving computational efficiency.Furthermore,we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations,enabling efficient processing across different sensor modalities.Specifically,for resource-constrained UAV platforms,this method significantly reduces computational cost,making it highly suitable for real-time UAV applications.展开更多
To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units(IMU),this paper proposes amulti-sensor fusion attitude estimationmethod based on an improved Error-State Kalman Fil...To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units(IMU),this paper proposes amulti-sensor fusion attitude estimationmethod based on an improved Error-State Kalman Filter(ESKF).Several adaptive mechanisms are introduced within the standard ESKF framework:first,the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions;second,the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time,mitigating disturbances caused by external accelerations and magnetic fields.Additionally,a dual-mode correction strategy is proposed for yaw angle estimation:a computationally efficient quaternion-based direct correction method is used for small-angle errors,while the system switches to a higher-precision adaptive ESKF algorithm for large-angle deviations.This strategy ensures estimation accuracy while effectively reducing computational complexity.Experimental results in mixed static-dynamic scenarios show that the proposed algorithmachieves the lowest rootmean square error(RMSE)in roll(5.638°)and yaw(6.315°),and ranks first in pitch(2.616°),validating the effectiveness of the improvements.In magnetic interference tests,it delivers the best overall performance,achieving the highest accuracy in roll and yaw and near-optimal performance in pitch,highlighting its excellent anti-interference capability and dynamic tracking performance.Complexity analysis further confirms a significant reduction in computational time compared to the standard ESKF.The results consistently demonstrate that the proposed method offers higher estimation accuracy and robustness under complex conditions,making it suitable for practical applications involving magnetic disturbances and rapid motions.展开更多
在车辆高速剧烈运动场景下,现有激光雷达-惯性里程计(LiDAR-inertial odometry,LIO)因IMU前向传播误差的快速累积,导致车辆的运动畸变补偿精度下降,进而引发"补偿误差-配准误差-状态估计误差"的级联效应,最终造成车辆定位轨...在车辆高速剧烈运动场景下,现有激光雷达-惯性里程计(LiDAR-inertial odometry,LIO)因IMU前向传播误差的快速累积,导致车辆的运动畸变补偿精度下降,进而引发"补偿误差-配准误差-状态估计误差"的级联效应,最终造成车辆定位轨迹显著偏离真实状态,本文提出了基于迭代误差卡尔曼滤波(iterated error-state Kalman filter,IESKF)的自适应激光雷达-惯性里程计(state-adaptive update LiDAR-inertial odometry,SAU-LIO)。首先,提出基于协方差特征值阈值的动态调整策略,以实时监测LIO误差累积趋势,自适应缩短状态更新时间间隔,有效抑制剧烈运动下的误差发散;其次,结合线特征与面特征的联合提取策略,构建概率观测模型,通过观测协方差矩阵约束实现不同置信度特征的最优加权融合,实现环境特征的有效利用。最后,基于NCLT(the university of Michigan north campus long-term vision and LIDAR dataset)、UTBM(EU long-term dataset with multiple sensors for autonomous driving)标准数据集及实车试验平台的验证结果表明:SAU-LIO算法在保证实时性的前提下,与对比算法相比具有更高的定位精度,在低速工况下,平均定位误差较次优的对比算法减小14.3%,在组合工况下,平均定位误差较次优的对比算法减小9.4%。展开更多
针对基于数据分发服务的分散式组网导航系统(decentralized networked navigation system based on DDS,DDS-DNNS)单定位节点状态估计问题,考虑节点能量约束及传感器增益退化,以Bayes理论为基础,设计了具有随机事件触发机制(stochastic ...针对基于数据分发服务的分散式组网导航系统(decentralized networked navigation system based on DDS,DDS-DNNS)单定位节点状态估计问题,考虑节点能量约束及传感器增益退化,以Bayes理论为基础,设计了具有随机事件触发机制(stochastic event-triggered,SET)的DDS-DNNS最小均方误差状态估计器。其中,SET机制通过比较是否传输测量值对应的后验估计的差异来决定测量值的重要程度。以此为基础,选取Wasserstein距离作为度量来表示后验估计的差异,并利用Wasserstein距离的性质及Bayes定理证明了后验估计是Gaussian的,从而得到了估计器的类Kalman滤波递推形式以及SET机制的显式表达式。证明了估计器的预测误差协方差有界,且上界和下界均收敛,同时,证明了平均信息传输率有界并推导得到了上界和下界的表达式。利用算例仿真演示了如何通过平均信息传输率的上界和下界确定调整矩阵,模拟了SET机制中一阶矩信息和二阶矩信息对SET机制的影响,同时采用比较实验验证了估计器的有效性。展开更多
针对可见光通信(visible light communication,VLC)系统中用户分布动态变化及非理想信道状态信息(channel state information,CSI)反馈带来的性能退化问题,对两用户协作非正交多址接入(non-orthogonal multiple access,NOMA)VLC系统进...针对可见光通信(visible light communication,VLC)系统中用户分布动态变化及非理想信道状态信息(channel state information,CSI)反馈带来的性能退化问题,对两用户协作非正交多址接入(non-orthogonal multiple access,NOMA)VLC系统进行了研究。建立了基于远端用户位置分布的信道增益概率密度函数模型,并推导了系统误比特概率(bit error probability,BEP)的解析表达式。在此基础上,结合最大比合并(maximal ratio combining,MRC)技术,对协作与非协作系统性能进行了对比分析。仿真结果表明:随信噪比增加,采用MRC的协作系统能够显著提升系统性能,并有效降低BEP;在对比的L-脉冲位置调制(pulse position modulation,PPM)和开关键控(on-off keying,OOK)调制方式中,8-PPM表现最佳,其次为4-PPM与OOK,而2-PPM性能最差;系统性能随外环半径减小而改善,说明外环参数对动态分布用户的覆盖性能影响更为显著;此外,非理想CSI会导致误码率上升,验证了精确信道估计的重要性。所建立的动态信道模型和协作机制为NOMA-VLC系统在智能家居与工业物联网中的应用提供了理论支撑。展开更多
基金National Natural Science Foundation of China(Grant No.62266045)National Science and Technology Major Project of China(No.2022YFE0138600)。
文摘This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation accuracy and robustness.We employ a manifold-based optimization approach,leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices.The proposed ESKF framework ensures state variables remain near the origin,effectively mitigating singularity issues and enhancing numerical stability.Additionally,due to the small magnitude of state variables,second-order terms can be neglected,simplifying Jacobian matrix computation and improving computational efficiency.Furthermore,we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations,enabling efficient processing across different sensor modalities.Specifically,for resource-constrained UAV platforms,this method significantly reduces computational cost,making it highly suitable for real-time UAV applications.
文摘To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units(IMU),this paper proposes amulti-sensor fusion attitude estimationmethod based on an improved Error-State Kalman Filter(ESKF).Several adaptive mechanisms are introduced within the standard ESKF framework:first,the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions;second,the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time,mitigating disturbances caused by external accelerations and magnetic fields.Additionally,a dual-mode correction strategy is proposed for yaw angle estimation:a computationally efficient quaternion-based direct correction method is used for small-angle errors,while the system switches to a higher-precision adaptive ESKF algorithm for large-angle deviations.This strategy ensures estimation accuracy while effectively reducing computational complexity.Experimental results in mixed static-dynamic scenarios show that the proposed algorithmachieves the lowest rootmean square error(RMSE)in roll(5.638°)and yaw(6.315°),and ranks first in pitch(2.616°),validating the effectiveness of the improvements.In magnetic interference tests,it delivers the best overall performance,achieving the highest accuracy in roll and yaw and near-optimal performance in pitch,highlighting its excellent anti-interference capability and dynamic tracking performance.Complexity analysis further confirms a significant reduction in computational time compared to the standard ESKF.The results consistently demonstrate that the proposed method offers higher estimation accuracy and robustness under complex conditions,making it suitable for practical applications involving magnetic disturbances and rapid motions.
文摘在车辆高速剧烈运动场景下,现有激光雷达-惯性里程计(LiDAR-inertial odometry,LIO)因IMU前向传播误差的快速累积,导致车辆的运动畸变补偿精度下降,进而引发"补偿误差-配准误差-状态估计误差"的级联效应,最终造成车辆定位轨迹显著偏离真实状态,本文提出了基于迭代误差卡尔曼滤波(iterated error-state Kalman filter,IESKF)的自适应激光雷达-惯性里程计(state-adaptive update LiDAR-inertial odometry,SAU-LIO)。首先,提出基于协方差特征值阈值的动态调整策略,以实时监测LIO误差累积趋势,自适应缩短状态更新时间间隔,有效抑制剧烈运动下的误差发散;其次,结合线特征与面特征的联合提取策略,构建概率观测模型,通过观测协方差矩阵约束实现不同置信度特征的最优加权融合,实现环境特征的有效利用。最后,基于NCLT(the university of Michigan north campus long-term vision and LIDAR dataset)、UTBM(EU long-term dataset with multiple sensors for autonomous driving)标准数据集及实车试验平台的验证结果表明:SAU-LIO算法在保证实时性的前提下,与对比算法相比具有更高的定位精度,在低速工况下,平均定位误差较次优的对比算法减小14.3%,在组合工况下,平均定位误差较次优的对比算法减小9.4%。
文摘针对基于数据分发服务的分散式组网导航系统(decentralized networked navigation system based on DDS,DDS-DNNS)单定位节点状态估计问题,考虑节点能量约束及传感器增益退化,以Bayes理论为基础,设计了具有随机事件触发机制(stochastic event-triggered,SET)的DDS-DNNS最小均方误差状态估计器。其中,SET机制通过比较是否传输测量值对应的后验估计的差异来决定测量值的重要程度。以此为基础,选取Wasserstein距离作为度量来表示后验估计的差异,并利用Wasserstein距离的性质及Bayes定理证明了后验估计是Gaussian的,从而得到了估计器的类Kalman滤波递推形式以及SET机制的显式表达式。证明了估计器的预测误差协方差有界,且上界和下界均收敛,同时,证明了平均信息传输率有界并推导得到了上界和下界的表达式。利用算例仿真演示了如何通过平均信息传输率的上界和下界确定调整矩阵,模拟了SET机制中一阶矩信息和二阶矩信息对SET机制的影响,同时采用比较实验验证了估计器的有效性。
文摘针对可见光通信(visible light communication,VLC)系统中用户分布动态变化及非理想信道状态信息(channel state information,CSI)反馈带来的性能退化问题,对两用户协作非正交多址接入(non-orthogonal multiple access,NOMA)VLC系统进行了研究。建立了基于远端用户位置分布的信道增益概率密度函数模型,并推导了系统误比特概率(bit error probability,BEP)的解析表达式。在此基础上,结合最大比合并(maximal ratio combining,MRC)技术,对协作与非协作系统性能进行了对比分析。仿真结果表明:随信噪比增加,采用MRC的协作系统能够显著提升系统性能,并有效降低BEP;在对比的L-脉冲位置调制(pulse position modulation,PPM)和开关键控(on-off keying,OOK)调制方式中,8-PPM表现最佳,其次为4-PPM与OOK,而2-PPM性能最差;系统性能随外环半径减小而改善,说明外环参数对动态分布用户的覆盖性能影响更为显著;此外,非理想CSI会导致误码率上升,验证了精确信道估计的重要性。所建立的动态信道模型和协作机制为NOMA-VLC系统在智能家居与工业物联网中的应用提供了理论支撑。
