Autocovariance Least Squares Noise Covariance Estimation for a Gust Load Alleviation Test-Bed

2022 ◽  
Author(s):  
Kimberly A. Hinson ◽  
Kristi A. Morgansen ◽  
Eli Livne
Keyword(s):  
Least Squares ◽  
Covariance Estimation ◽  
Test Bed ◽  
Noise Covariance ◽  
Gust Load Alleviation

Robust autocovariance least-squares noise covariance estimation algorithm

Measurement ◽  
2021 ◽  
pp. 110331
Author(s):  
Wei Li ◽  
Xu Lin ◽  
Shaoda Li ◽  
Jiang Ye ◽  
Chaolong Yao ◽  
...  
Keyword(s):  
Least Squares ◽  
Estimation Algorithm ◽  
Covariance Estimation ◽  
Noise Covariance

scholarly journals Improved Noise Covariance Estimation in Visual Servoing Using an Autocovariance Least-squares Approach

2019 ◽  
Vol 52 (22) ◽  
pp. 37-42 ◽  
Author(s):  
Jasper Brown ◽  
Daobilige Su ◽  
He Kong ◽  
Salah Sukkarieh ◽  
Eric Kerrigan
Keyword(s):  
Least Squares ◽  
Visual Servoing ◽  
Covariance Estimation ◽  
Noise Covariance ◽  
Least Squares Approach

scholarly journals Recursive Generalized Total Least Squares with Noise Covariance Estimation

2014 ◽  
Vol 47 (3) ◽  
pp. 4637-4643 ◽  
Author(s):  
Stephan Rhode ◽  
Felix Bleimund ◽  
Frank Gauterin
Keyword(s):  
Least Squares ◽  
Total Least Squares ◽  
Covariance Estimation ◽  
Noise Covariance

Improved noise covariance estimation in visual servoing using an autocovariance least-squares approach

Mechatronics ◽  
2020 ◽  
Vol 68 ◽  
pp. 102381
Author(s):  
Jasper Brown ◽  
Daobilige Su ◽  
He Kong ◽  
Salah Sukkarieh ◽  
Eric C. Kerrigan
Keyword(s):  
Least Squares ◽  
Visual Servoing ◽  
Covariance Estimation ◽  
Noise Covariance ◽  
Least Squares Approach

scholarly journals Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Vladimir Shin ◽  
Rebbecca T. Y. Thien ◽  
Yoonsoo Kim
Keyword(s):  
Least Squares ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Receding Horizon ◽  
Covariance Estimator ◽  
Noise Covariance ◽  
Experimental Errors ◽  
And Performance ◽  
Theoretical Results ◽  
Selection Of

This paper presents a noise covariance estimation method for dynamical models with rectangular noise gain matrices. A novel receding horizon least squares criterion to achieve high estimation accuracy and stability under environmental uncertainties and experimental errors is proposed. The solution to the optimization problem for the proposed criterion gives equations for a novel covariance estimator. The estimator uses a set of recent information with appropriately chosen horizon conditions. Of special interest is a constant rectangular noise gain matrices for which the key theoretical results are obtained. They include derivation of a recursive form for the receding horizon covariance estimator and iteration procedure for selection of the best horizon length. Efficiency of the covariance estimator is demonstrated through its implementation and performance on dynamical systems with an arbitrary number of process and measurement noises.

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