Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application areas.This paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy cri...Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application areas.This paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy criterion(MCC)instead of the minimummean square error criterion(MMSE).This innovative approach is applied to the loose coupling of the Inertial Navigation System(INS)and Ultra-Wideband(UWB).By introducing the maximum correntropy criterion,the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise,thus enhancing its adaptability to diverse environmental localization requirements.Particularly in the presence of non-Gaussian noise,especially heavy-tailed noise,the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF.The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform(UT)and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC.Subsequently,the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations.Moreover,to mitigate the influence of non-line-of-sight(NLOS)errors positioning accuracy,this paper proposes a k-medoid clustering algorithm based on bisection k-means(Bikmeans).This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation.Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.展开更多
针对无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计锂电池荷电状态(state of charge,SOC)时精度低、稳定性差、产生的sigma点过多导致计算难度大等不足,提出一种基于自适应球形不敏变换方式的无迹卡尔曼滤波(unscented Kalman f...针对无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计锂电池荷电状态(state of charge,SOC)时精度低、稳定性差、产生的sigma点过多导致计算难度大等不足,提出一种基于自适应球形不敏变换方式的无迹卡尔曼滤波(unscented Kalman filter based on adaptive spherical insensitive transformation,ASIT-UKF)算法。该算法通过使用球形不敏变换方式选择权系数以及初始化一元向量对sigma点的产生进行选取。与UKF算法相比,ASIT-UKF算法产生的sigma点减少近50%,使得算法的计算复杂度大大降低。同时,将产生的所有sigma点进行单位球形面上的归一化处理,提高了数值的稳定性。考虑到实际运行中锂电池系统噪声干扰带来的不确定性,加入Sage-Husa自适应滤波器对不确定性噪声的干扰进行实时更新和修正,以达到提高在线锂电池SOC估计精度的目的。最后,将均方根误差和最大绝对误差计算公式引入到性能估计指标中。实验结果表明,ASIT-UKF算法在准确度、鲁棒性和收敛性方面具有优越的性能。展开更多
Based on a uniform linear array, a new widely linear unscented Kalman filter-based constant modulus algorithm (WL-UKF-CMA) for blind adaptive beamforming is proposed. The new algorithm is designed according to the con...Based on a uniform linear array, a new widely linear unscented Kalman filter-based constant modulus algorithm (WL-UKF-CMA) for blind adaptive beamforming is proposed. The new algorithm is designed according to the constant modulus criterion and takes full advantage of the noncircular property of the signal of interest (SOI), significantly increasing the output signal-to interference-plus-noise ratio (SINR), enhancing the convergence speed and decreasing the steady-state misadjustment. Since it requires no known training data, the proposed algorithm saves a large amount of the available spectrum. Theoretical analysis and simulation results are presented to demonstrate its superiority over the conventional linear least mean square-based CMA (L-LMS-CMA), the conventional linear recursive least square-based CMA (L-RLS-CMA), WL-LMS-CMA, WL-RLS-CMA and L-UKF-CMA.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.62273083 and 61803077Natural Science Foundation of Hebei Province under Grant No.F2020501012.
文摘Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application areas.This paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy criterion(MCC)instead of the minimummean square error criterion(MMSE).This innovative approach is applied to the loose coupling of the Inertial Navigation System(INS)and Ultra-Wideband(UWB).By introducing the maximum correntropy criterion,the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise,thus enhancing its adaptability to diverse environmental localization requirements.Particularly in the presence of non-Gaussian noise,especially heavy-tailed noise,the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF.The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform(UT)and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC.Subsequently,the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations.Moreover,to mitigate the influence of non-line-of-sight(NLOS)errors positioning accuracy,this paper proposes a k-medoid clustering algorithm based on bisection k-means(Bikmeans).This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation.Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.
文摘针对无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计锂电池荷电状态(state of charge,SOC)时精度低、稳定性差、产生的sigma点过多导致计算难度大等不足,提出一种基于自适应球形不敏变换方式的无迹卡尔曼滤波(unscented Kalman filter based on adaptive spherical insensitive transformation,ASIT-UKF)算法。该算法通过使用球形不敏变换方式选择权系数以及初始化一元向量对sigma点的产生进行选取。与UKF算法相比,ASIT-UKF算法产生的sigma点减少近50%,使得算法的计算复杂度大大降低。同时,将产生的所有sigma点进行单位球形面上的归一化处理,提高了数值的稳定性。考虑到实际运行中锂电池系统噪声干扰带来的不确定性,加入Sage-Husa自适应滤波器对不确定性噪声的干扰进行实时更新和修正,以达到提高在线锂电池SOC估计精度的目的。最后,将均方根误差和最大绝对误差计算公式引入到性能估计指标中。实验结果表明,ASIT-UKF算法在准确度、鲁棒性和收敛性方面具有优越的性能。
基金supported by the National Natural Science Foundation of China(61573113)the Harbin Science and Technology Innovation Talents(Excellent Discipline Leader)Research Fund(2014RFXXJ074)the National Scholarship([2016]3100)
文摘Based on a uniform linear array, a new widely linear unscented Kalman filter-based constant modulus algorithm (WL-UKF-CMA) for blind adaptive beamforming is proposed. The new algorithm is designed according to the constant modulus criterion and takes full advantage of the noncircular property of the signal of interest (SOI), significantly increasing the output signal-to interference-plus-noise ratio (SINR), enhancing the convergence speed and decreasing the steady-state misadjustment. Since it requires no known training data, the proposed algorithm saves a large amount of the available spectrum. Theoretical analysis and simulation results are presented to demonstrate its superiority over the conventional linear least mean square-based CMA (L-LMS-CMA), the conventional linear recursive least square-based CMA (L-RLS-CMA), WL-LMS-CMA, WL-RLS-CMA and L-UKF-CMA.
