Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Bayes and empirical Bayes semi-blind deconvolution using eigenfunctions of a prior covariance
Automatica (Journal of IFAC)
Robust Kalman filters for linear time-varying systems withstochastic parametric uncertainties
IEEE Transactions on Signal Processing
A least-squares approach to blind channel identification
IEEE Transactions on Signal Processing
Fast maximum likelihood for blind identification of multiple FIRchannels
IEEE Transactions on Signal Processing
Brief paper: Optimal estimation of linear discrete-time systems with stochastic parameters
Automatica (Journal of IFAC)
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This paper develops a Kalman smoother to estimate white noise input for systems with random parametric uncertainties in the observation equation. The derived input estimator is optimal in terms of the mean square error (MSE) criterion. Convergence analysis for the derived Kalman smoother is provided, which shows that stability of the Kalman filter cannot guarantee that of the designed fixed-point Kalman smoother. Furthermore, the designed smoothing estimator is applied to the semi-blind deconvolution problem, and an optimal solution is obtained. Numerical examples are given to demonstrate the performance of the proposed method in comparison with two typical deconvolution methods.