Sentence examples for minimum variance estimation from inspiring English sources

The proposed refined instrumental variable algorithm achieves minimum variance estimation of the process model parameters.

Compared with minimum variance estimation (MVE) method based on interpolation techniques, the proposed method could obtain superior homodyne detection efficiency with lower operation complexity.

The optimal fault detection is then realized to provide the timely and optimal detection of potential problems by adopting these real-time minimum variance estimation schemes.

This algorithm can perform optimal minimum variance estimation on SOC and facilitate the prediction and estimation on battery at a certain moment in the future.

More specifically, to generate residual signals with a minimum variance, minimum variance estimation is first addressed in terms of recursive least square (RLS) and Kalman filter by iterative interactions with the process environment.

This method is based on the Refined Instrumental Variable (RIV) algorithm which, because of an appropriate choice of particular design variables, achieves minimum variance estimation of the model parameters.

This article also verifies that the proposed estimation method becomes a minimum variance estimator (MVE) in the expanded phase domain.

Nevertheless, the resulting EKF is a practical approximation to the minimum variance estimator when the state equation is nonlinear, and will be shown to provide a good performance in time-varying channel estimation.

We address the estimation problem by a maximum-likelihood approach, which is known to yield optimal (unbiased and minimum variance) estimates for our problem setting in the case where Y is fully known.

Such estimates are also called minimum variance estimates.

A parameterized three-stage Kalman filter (PTSKF) is proposed, serving as a unified solution to unbiased minimum-variance estimation for systems with unknown inputs that affect both the system and the outputs.