Discussion and analysis on alignment error model of laser communication

2018 ◽  
Author(s):  
Yan An ◽  
Yujing Su ◽  
Qi Xin ◽  
Yiyun Xu ◽  
Keyan Dong
Keyword(s):  
Error Model ◽  
Laser Communication ◽  
Alignment Error

scholarly journals Alignment Error Models and Ensemble-Based Data Assimilation

2005 ◽  
Vol 133 (6) ◽  
pp. 1687-1709 ◽  
Author(s):  
W. Gregory Lawson ◽  
James A. Hansen
Keyword(s):  
Minimum Variance ◽  
Error Model ◽  
Alignment Error ◽  
Error Models ◽  
Wide Range ◽  
Estimation Problems ◽  
Estimation Scheme ◽  
Non Gaussian ◽  
The Individual ◽  
Alignment Errors

Abstract The concept of alternative error models is suggested as a means to redefine estimation problems with non-Gaussian additive errors so that familiar and near-optimal Gaussian-based methods may still be applied successfully. The specific example of a mixed error model including both alignment errors and additive errors is examined. Using the specific form of a soliton, an analytical solution to the Korteweg–de Vries equation, the total (additive) errors of states following the mixed error model are demonstrably non-Gaussian for large enough alignment errors, and an ensemble of such states is handled poorly by a traditional ensemble Kalman filter, even if position observations are included. Consideration of the mixed error model itself naturally suggests a two-step approach to state estimation where the alignment errors are corrected first, followed by application of an estimation scheme to the remaining additive errors, the first step aimed at removing most of the non-Gaussianity so the second step can proceed successfully. Taking an ensemble approach for the soliton states in a perfect-model scenario, this two-step approach shows a great improvement over traditional methods in a wide range of observational densities, observing frequencies, and observational accuracies. In cases where the two-step approach is not successful, it is often attributable to the first step not having sufficiently removed the non-Gaussianity, indicating the problem strictly requires an estimation scheme that does not make Gaussian assumptions. However, in these cases a convenient approximation to the two-step approach is available, which trades obtaining a minimum variance estimate ensemble mean for more physically sound updates of the individual ensemble members.

Cup Alignment Error Model for Total Hip Arthroplasty

2005 ◽  
Vol &NA; (437) ◽  
pp. 132-137 ◽  
Author(s):  
Alon Wolf ◽  
Anthony M DiGioia ◽  
Andrew B Mor ◽  
Branislav Jaramaz
Keyword(s):  
Total Hip Arthroplasty ◽  
Hip Arthroplasty ◽  
Error Model ◽  
Alignment Error ◽  
Total Hip

Cancelable Biometric System for face Based on linear minimum mean square error Model.

2019 ◽  
Vol 28 (1) ◽  
pp. 145-152
Author(s):  
Abd El-aziz Ebrahim Hsaneen ◽  
EL-Sayed M. El-Rabaei ◽  
Moawad I. Dessouky ◽  
Ghada El-bamby ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Error Model ◽  
Mean Square ◽  
Biometric System
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