The main cause of dynamic errors is due to frequency response limitation of measurement system. One way of solving this problem is designing an effective inverse filter. Since the problem is ill-conditioned, a small u...The main cause of dynamic errors is due to frequency response limitation of measurement system. One way of solving this problem is designing an effective inverse filter. Since the problem is ill-conditioned, a small uncertainty in the measurement will came large deviation in reconstncted signals. The amplified noise has to be suppressed at the sacrifice of biasing in estimation. The paper presents a kind of designing method of inverse filter in frequency domain based on stabilized solutions of Fredholm integral equations of the fast kind in order to reduce dynamic errors. Compared with previous several work, the method has advantage of generalization. Simulations with different Signal-to-Noise ratio （SNR） are investigated. Flexibility of the method is verified. Application of correcting dynamic error is given.展开更多
The authors consider the problem of estimating the ordered means of two normal distributions with unknown ordered variances. The authors discuss the estimation of two ordered means, individually, in terms of stochasti...The authors consider the problem of estimating the ordered means of two normal distributions with unknown ordered variances. The authors discuss the estimation of two ordered means, individually, in terms of stochastic domination and MSE （mean squared error）. The authors show that in estimating the mean with larger variance, the usual estimator under order restriction on means can be improved upon. However, in estimating the mean with smaller variance, the usual estimator can＇t be improved upon even under MSE. The authors also discuss simultaneous estimation problem of two ordered means when unknown variances are ordered.展开更多
For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cro...For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.展开更多
We consider an Error-in-Variable partially linear model where the covariates of linear part are measured with error which follows a normal distribution with a known covariance matrix. We propose a corrected-loss estim...We consider an Error-in-Variable partially linear model where the covariates of linear part are measured with error which follows a normal distribution with a known covariance matrix. We propose a corrected-loss estimation of the covariate effect. The proposed estimator is asymptotically normal. Simulation studies are presented to show that the proposed method performs well with finite samples, and the proposed method is applied to a real data set.展开更多
基金The paper is sponsored by National Natural Science Foundation of China(No.50675211)Natural Science Foundation(No.2009011023)Returned Overseas Graduates Foundation(No.2008067) of Shanxi Provincein China
文摘The main cause of dynamic errors is due to frequency response limitation of measurement system. One way of solving this problem is designing an effective inverse filter. Since the problem is ill-conditioned, a small uncertainty in the measurement will came large deviation in reconstncted signals. The amplified noise has to be suppressed at the sacrifice of biasing in estimation. The paper presents a kind of designing method of inverse filter in frequency domain based on stabilized solutions of Fredholm integral equations of the fast kind in order to reduce dynamic errors. Compared with previous several work, the method has advantage of generalization. Simulations with different Signal-to-Noise ratio （SNR） are investigated. Flexibility of the method is verified. Application of correcting dynamic error is given.
文摘The authors consider the problem of estimating the ordered means of two normal distributions with unknown ordered variances. The authors discuss the estimation of two ordered means, individually, in terms of stochastic domination and MSE （mean squared error）. The authors show that in estimating the mean with larger variance, the usual estimator under order restriction on means can be improved upon. However, in estimating the mean with smaller variance, the usual estimator can＇t be improved upon even under MSE. The authors also discuss simultaneous estimation problem of two ordered means when unknown variances are ordered.
基金Supported by the National Natural Science Foundation of China (No.60874063)Science and Technology Research Foundation of Heilongjiang Education Department (No.11521214)Open Fund of Key Laboratory of Electronics Engineering, College of Heilongjiang Province (Heilongjiang University)
文摘For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.
基金supported by National Natural Science Foundation of China(Grant Nos.10901020 and 11371062)the Fundamental Research Funds for the Central Universities,Beijing Center for Mathematics and Information Interdisciplinary Sciences,China Zhongdian Project(Grant No.11131002)
文摘We consider an Error-in-Variable partially linear model where the covariates of linear part are measured with error which follows a normal distribution with a known covariance matrix. We propose a corrected-loss estimation of the covariate effect. The proposed estimator is asymptotically normal. Simulation studies are presented to show that the proposed method performs well with finite samples, and the proposed method is applied to a real data set.
