scholarly journals Comparison Study and Sensitivity Analysis of Flight Test Techniques for Air Data Position Error Correction in Small Aircraft

2011 ◽  
Vol 53 (182) ◽  
pp. 250-257
Author(s):  
Sang-Jong LEE ◽  
Jae Won CHANG ◽  
Jeong Ho PARK ◽  
Byoung Soo KIM ◽  
Kie Jeong SEONG
Keyword(s):  
Sensitivity Analysis ◽  
Error Correction ◽  
Flight Test ◽  
Position Error ◽  
Comparison Study ◽  
Small Aircraft

Sampling Error Correction Evaluated Using a Convective-Scale 1000-Member Ensemble

2019 ◽  
Vol 148 (3) ◽  
pp. 1229-1249 ◽  
Author(s):  
Tobias Necker ◽  
Martin Weissmann ◽  
Yvonne Ruckstuhl ◽  
Jeffrey Anderson ◽  
Takemasa Miyoshi
Keyword(s):  
Sensitivity Analysis ◽  
Data Assimilation ◽  
Error Correction ◽  
Degrees Of Freedom ◽  
Sampling Error ◽  
Lookup Table ◽  
Ensemble Prediction ◽  
Sampling Errors ◽  
Convective Scale ◽  
The Impact

Abstract State-of-the-art ensemble prediction systems usually provide ensembles with only 20–250 members for estimating the uncertainty of the forecast and its spatial and spatiotemporal covariance. Given that the degrees of freedom of atmospheric models are several magnitudes higher, the estimates are therefore substantially affected by sampling errors. For error covariances, spurious correlations lead to random sampling errors, but also a systematic overestimation of the correlation. A common approach to mitigate the impact of sampling errors for data assimilation is to localize correlations. However, this is a challenging task given that physical correlations in the atmosphere can extend over long distances. Besides data assimilation, sampling errors pose an issue for the investigation of spatiotemporal correlations using ensemble sensitivity analysis. Our study evaluates a statistical approach for correcting sampling errors. The applied sampling error correction is a lookup table–based approach and therefore computationally very efficient. We show that this approach substantially improves both the estimates of spatial correlations for data assimilation as well as spatiotemporal correlations for ensemble sensitivity analysis. The evaluation is performed using the first convective-scale 1000-member ensemble simulation for central Europe. Correlations of the 1000-member ensemble forecast serve as truth to assess the performance of the sampling error correction for smaller subsets of the full ensemble. The sampling error correction strongly reduced both random and systematic errors for all evaluated variables, ensemble sizes, and lead times.

scholarly journals Analysis and Correction of the Magnetometer’s Position Error in a Cross-Shaped Magnetic Tensor Gradiometer

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1290
Author(s):  
Youyu Yan ◽  
Yan Ma ◽  
Jianguo Liu
Keyword(s):  
Error Correction ◽  
Magnetic Moment ◽  
Correction Method ◽  
Position Error ◽  
Dipole Source ◽  
Gradient Tensor ◽  
Magnetic Gradient ◽  
Error Correction Method ◽  
Locating Error ◽  
The Relationship

When using the technique of magnetic gradient tensor measurements to obtain the position of magnetic objects, calibration of the magnetic tensor gradiometer plays a pivotal role in precisely locating the target, and extensive research has been carried out on this up to now. However, previous studies have always lacked sufficient discussion on the position error of magnetometers in magnetic tensor gradiometers caused by inaccurate installment of magnetometers. In this paper, we analyze and correct this position error based on a magnetic dipole source. The result of the simulation exemplifies that the magnetometer’s position error will affect the locating accuracy and, therefore, it is worth correcting this error. The relationship between position error and magnetic gradient tensor components is established, followed by an error correction method based on this relationship. Simulations illustrate that this method can effectively decrease the effect caused by the position error of magnetometers and improve the locating performance with locating error and magnetic moment errors dropping from 2 to 0.2 m and 6 × 10 5 A ⋅ m 2 to 5 × 10 4 A ⋅ m 2 , respectively.

scholarly journals Measurement error correction and sensitivity analysis in longitudinal dietary intervention studies using an external validation study

Biometrics ◽  
2019 ◽  
Vol 75 (3) ◽  
pp. 927-937 ◽  
Author(s):  
Juned Siddique ◽  
Michael J. Daniels ◽  
Raymond J. Carroll ◽  
Trivellore E. Raghunathan ◽  
Elizabeth A. Stuart ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Measurement Error ◽  
Error Correction ◽  
Validation Study ◽  
Intervention Studies ◽  
Dietary Intervention ◽  
External Validation ◽  
External Validation Study ◽  
Measurement Error Correction

Evaluation Modeling and Optimal Adjustment of Position Error Based on Localization Sensitivity Analysis

2011 ◽  
Vol 697-698 ◽  
pp. 258-261
Author(s):  
G.Y. He ◽  
C.X. Hu ◽  
X. Liu
Keyword(s):  
Sensitivity Analysis ◽  
Evaluation Model ◽  
Geometric Error ◽  
Position Error ◽  
Position Errors ◽  
Surface Deviation ◽  
Optimal Adjustment ◽  
Fixture System ◽  
System Errors ◽  
The Relationship

Sensitivity analysis is to evaluate how sensitive the surface deviation of a workpiece is to a geometric error of locator. With this thinking, the relationship between geometric error of locators and the position error of holes group is presented. The fixture system errors model and evaluation model of position errors are established. Furthermore with both models, a method of optimizing the position errors by adjusting the locators’ position is presented, which can get to accuracy localization. At last, a simulation study is used to verify the method.

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