Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matri...Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.展开更多
提出一种改进的基于数据块更新的递归主元分析(recursive principal component analysis,RPCA)方法,对具有慢时变和多变量等特性的某型舰空导弹武器雷达发射机工作过程进行自适应监测。该方法在协方差矩阵的特征值分解中引入低秩奇异值...提出一种改进的基于数据块更新的递归主元分析(recursive principal component analysis,RPCA)方法,对具有慢时变和多变量等特性的某型舰空导弹武器雷达发射机工作过程进行自适应监测。该方法在协方差矩阵的特征值分解中引入低秩奇异值分解递归方法,实现负荷矩阵和特征值矩阵的递归计算;制定了均值、方差的更新策略;给出一种基于指数加权的控制限递归算法以提高RPCA的健壮性。实验证明该方法能自适应地跟踪过程时变并实时监测故障,同时有效地降低误警率。展开更多
对于背景呈非线性变化的复杂图像,用背景预测的方法对红外点目标进行检测时,传统的线性最小二乘法(Least Squares,LS)的效果比较差。文章使用核方法(Kernel Methods,KMs)推导了最小二乘法的非线性版本:核最小二乘算法(Kernel Least Squa...对于背景呈非线性变化的复杂图像,用背景预测的方法对红外点目标进行检测时,传统的线性最小二乘法(Least Squares,LS)的效果比较差。文章使用核方法(Kernel Methods,KMs)推导了最小二乘法的非线性版本:核最小二乘算法(Kernel Least Squares,KLS);进一步推导出了更适合动态系统时序预测的指数加权形式的核最小二乘算法(Kernel Exponential Weighted Least Squares,KEWLS)。提出了一种基于核方法的红外点目标检测算法,先用KEWLS非线性回归算法预测红外图像背景,再通过自适应门限检测残差图像中的目标。非线性函数回归和红外序列图像检测实验表明核方法较大地改进了算法的非线性函数估计与红外背景预测能力。展开更多
基金This work is supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX18_0467)Jiangsu Province,China.During the revision of this paper,the author is supported by China Scholarship Council(No.201906840021)China to continue some research related to data processing.
文摘Kalman filter is commonly used in data filtering and parameters estimation of nonlinear system,such as projectile's trajectory estimation and control.While there is a drawback that the prior error covariance matrix and filter parameters are difficult to be determined,which may result in filtering divergence.As to the problem that the accuracy of state estimation for nonlinear ballistic model strongly depends on its mathematical model,we improve the weighted least squares method(WLSM)with minimum model error principle.Invariant embedding method is adopted to solve the cost function including the model error.With the knowledge of measurement data and measurement error covariance matrix,we use gradient descent algorithm to determine the weighting matrix of model error.The uncertainty and linearization error of model are recursively estimated by the proposed method,thus achieving an online filtering estimation of the observations.Simulation results indicate that the proposed recursive estimation algorithm is insensitive to initial conditions and of good robustness.
文摘提出一种改进的基于数据块更新的递归主元分析(recursive principal component analysis,RPCA)方法,对具有慢时变和多变量等特性的某型舰空导弹武器雷达发射机工作过程进行自适应监测。该方法在协方差矩阵的特征值分解中引入低秩奇异值分解递归方法,实现负荷矩阵和特征值矩阵的递归计算;制定了均值、方差的更新策略;给出一种基于指数加权的控制限递归算法以提高RPCA的健壮性。实验证明该方法能自适应地跟踪过程时变并实时监测故障,同时有效地降低误警率。
文摘对于背景呈非线性变化的复杂图像,用背景预测的方法对红外点目标进行检测时,传统的线性最小二乘法(Least Squares,LS)的效果比较差。文章使用核方法(Kernel Methods,KMs)推导了最小二乘法的非线性版本:核最小二乘算法(Kernel Least Squares,KLS);进一步推导出了更适合动态系统时序预测的指数加权形式的核最小二乘算法(Kernel Exponential Weighted Least Squares,KEWLS)。提出了一种基于核方法的红外点目标检测算法,先用KEWLS非线性回归算法预测红外图像背景,再通过自适应门限检测残差图像中的目标。非线性函数回归和红外序列图像检测实验表明核方法较大地改进了算法的非线性函数估计与红外背景预测能力。
