The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration,communication and signal processing.By combining the Kalman filtering method with the modern ...The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration,communication and signal processing.By combining the Kalman filtering method with the modern time series analysis method,based on the autoregressive moving average(ARMA)innovation model,new distributed fusion white noise deconvolution estimators are presented by weighting local input white noise estimators for general multisensor systems with different local dynamic models and correlated noises.The new estimators can handle input white noise fused filtering,prediction and smoothing problems,and are applicable to systems with colored measurement noise.Their accuracy is higher than that of local white noise deconvolution estimators.To compute the optimal weights,the new formula for local estimation error cross-covariances is given.A Monte Carlo simulation for the system with Bernoulli-Gaussian input white noise shows their effectiveness and performance.展开更多
White noise deconvolution or input white noise estimation problem has important appli-cation backgrounds in oil seismic exploration,communication and signal processing.By the modern time series analysis method,based o...White noise deconvolution or input white noise estimation problem has important appli-cation backgrounds in oil seismic exploration,communication and signal processing.By the modern time series analysis method,based on the Auto-Regressive Moving Average(ARMA) innovation model,under the linear minimum variance optimal fusion rules,three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises.They can handle the input white noise fused filtering,prediction and smoothing problems.The accuracy of the fusers is higher than that of each local white noise estimator.In order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.展开更多
针对传统Unscented卡尔曼滤波器(Unscented Kalman filter,UKF)在噪声先验统计未知时变情况下非线性滤波精度下降甚至发散的问题,设计了一种带噪声统计估计器的自适应UKF滤波算法.首先根据极大后验(Maximum a posterior,MAP)估计原理,...针对传统Unscented卡尔曼滤波器(Unscented Kalman filter,UKF)在噪声先验统计未知时变情况下非线性滤波精度下降甚至发散的问题,设计了一种带噪声统计估计器的自适应UKF滤波算法.首先根据极大后验(Maximum a posterior,MAP)估计原理,推导出一种次优无偏MAP常值噪声统计估计器;接着在此基础之上,采用指数加权的方法,给出了时变噪声统计估计器的递推公式;最后对自适应UKF算法进行了性能分析.相比于传统UKF,该自适应UKF算法在噪声统计未知时变情况下不仅滤波依然收敛,滤波精度及稳定性显著提高,而且其具有应对噪声变化的自适应能力.仿真实例验证了其有效性.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.60874063)the Science and Technology Research Foundation of Heilongjiang Education Department (No.11523037)the Automatic Control Key Laboratory of Heilongjiang University.
文摘The white noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration,communication and signal processing.By combining the Kalman filtering method with the modern time series analysis method,based on the autoregressive moving average(ARMA)innovation model,new distributed fusion white noise deconvolution estimators are presented by weighting local input white noise estimators for general multisensor systems with different local dynamic models and correlated noises.The new estimators can handle input white noise fused filtering,prediction and smoothing problems,and are applicable to systems with colored measurement noise.Their accuracy is higher than that of local white noise deconvolution estimators.To compute the optimal weights,the new formula for local estimation error cross-covariances is given.A Monte Carlo simulation for the system with Bernoulli-Gaussian input white noise shows their effectiveness and performance.
基金Supported by the National Natural Science Foundation of China (No.60874063)Science and Technology Re-search Foundation of Heilongjiang Education Department (No.11523037)
文摘White noise deconvolution or input white noise estimation problem has important appli-cation backgrounds in oil seismic exploration,communication and signal processing.By the modern time series analysis method,based on the Auto-Regressive Moving Average(ARMA) innovation model,under the linear minimum variance optimal fusion rules,three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with time-delayed measurements and colored measurement noises.They can handle the input white noise fused filtering,prediction and smoothing problems.The accuracy of the fusers is higher than that of each local white noise estimator.In order to compute the optimal weights,the formula of computing the local estimation error cross-covariances is given.A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.
文摘针对传统Unscented卡尔曼滤波器(Unscented Kalman filter,UKF)在噪声先验统计未知时变情况下非线性滤波精度下降甚至发散的问题,设计了一种带噪声统计估计器的自适应UKF滤波算法.首先根据极大后验(Maximum a posterior,MAP)估计原理,推导出一种次优无偏MAP常值噪声统计估计器;接着在此基础之上,采用指数加权的方法,给出了时变噪声统计估计器的递推公式;最后对自适应UKF算法进行了性能分析.相比于传统UKF,该自适应UKF算法在噪声统计未知时变情况下不仅滤波依然收敛,滤波精度及稳定性显著提高,而且其具有应对噪声变化的自适应能力.仿真实例验证了其有效性.
