Robust estimation of measurement error covariance

2002 ◽  
Author(s):  
Zhao Yuhong ◽  
Gu Zhongwen
Keyword(s):  
Measurement Error ◽  
Robust Estimation ◽  
Error Covariance

scholarly journals Minimax Robust Optimal Estimation Fusion for Distributed Multisensor Systems with a Relative Entropy Uncertainty

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Hua Li ◽  
Jie Zhou
Keyword(s):  
Robust Estimation ◽  
Relative Entropy ◽  
Estimation Error ◽  
Optimal Estimation ◽  
Minimum Variance ◽  
Estimation Errors ◽  
Multisensor Systems ◽  
Error Covariance ◽  
Local Estimation ◽  
Estimation Fusion

This paper considers the robust estimation fusion problem for distributed multisensor systems with uncertain correlations of local estimation errors. For an uncertain class characterized by the Kullback-Leibler (KL) divergence from the actual model to nominal model of local estimation error covariance, the robust estimation fusion problem is formulated to find a linear minimum variance unbiased estimator for the least favorable model. It is proved that the optimal fuser under nominal correlation model is robust while the estimation error has a relative entropy uncertainty.

scholarly journals An Error Evaluation Method for the Ship Angular Flexure Measurement Based on the Principle of Relevance

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yutong Zhang ◽  
Xianglu Ma ◽  
Shiqiao Qin ◽  
Wei Wu ◽  
Wenfeng Tan
Keyword(s):  
Measurement Error ◽  
Evaluation Method ◽  
Estimation Error ◽  
Good Prediction ◽  
Error Evaluation ◽  
Prediction Formula ◽  
Matching Method ◽  
Error Covariance ◽  
Velocity Matching ◽  
Flexure Angle

Due to the lack of true ship angular flexure data, it is difficult to evaluate its measurement error of the angular velocity matching method in practice. In this paper, the cause of the measurement error of the ship flexure angle is analyzed in theory, and an evaluation method for the ship angular flexure measurement error based on the principle of relevance is proposed. The proposed method provides a prediction formula to describe the estimation error of the static flexure angle based on the off-diagonal elements of the error covariance matrix P in Kalman filtering. In addition, the optimized coefficient F is introduced to make the prediction error range better describe the real error variation. The optimized coefficient F ensures that the proposed formula has good prediction effects in all three directions. Simulations based on the actual measured ship flexure data are carried out, and the simulation results verify the capability of the prediction formula. The proposed method can be used in the evaluation of the ship flexure measurement error.

scholarly journals Enabling Robust State Estimation Through Measurement Error Covariance Adaptation

2020 ◽  
Vol 56 (3) ◽  
pp. 2026-2040 ◽  
Author(s):  
Ryan M. Watson ◽  
Jason N. Gross ◽  
Clark N. Taylor ◽  
Robert C. Leishman
Keyword(s):  
Measurement Error ◽  
State Estimation ◽  
Error Covariance

Vibration-Robust Attitude and Heading Reference System Using Windowed Measurement Error Covariance

2017 ◽  
Vol 18 (3) ◽  
pp. 555-564
Author(s):  
Jong-Myeong Kim ◽  
Sung-Hoon Mok ◽  
Henzeh Leeghim ◽  
Chang-Yull Lee
Keyword(s):  
Measurement Error ◽  
Reference System ◽  
Error Covariance

Robust Estimation with Nonrandom Measurement Error and Weak Instruments

2013 ◽  
Vol 21 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Timm Betz
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Robust Estimation ◽  
Unified Framework ◽  
Democratic Transitions ◽  
Economic Shocks ◽  
Weak Instruments ◽  
Monte Carlo Studies ◽  
Common Problems ◽  
Practical Implications

Two common problems in applications of two-stage least squares (2SLS) are nonrandom measurement error in the endogenous variable and weak instruments. In the presence of nonrandom measurement error, 2SLS yields inconsistent estimates. In the presence of weak instruments, confidence intervals andp-values can be severely misleading. This article introduces a rank-based estimator, grounded in randomization inference, which addresses both problems within a unified framework. Monte Carlo studies illustrate the deficiencies of 2SLS and the virtues of the rank-based estimator in terms of bias and efficiency. A replication of a study of the effect of economic shocks on democratic transitions demonstrates the practical implications of accounting for nonrandom measurement error and weak instruments.

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