Analysis of powers-of-two calculations of the Allan variance and their relation to the standard variance

2019 ◽  
Author(s):  
Noah Schlossberger ◽  
David Howe
Keyword(s):  
Allan Variance ◽  
Standard Variance

scholarly journals Characterization of error sources in an inertial unit using Allan variance and nonlinear parameter adjustment

2020 ◽  
Vol 1674 ◽  
pp. 012004
Author(s):  
W A Hernandez ◽  
J S Castillo-Corredor ◽  
J A Ramos-Cifuentes ◽  
F Fuentes ◽  
L F Castañeda
Keyword(s):  
Allan Variance ◽  
Nonlinear Parameter ◽  
Error Sources ◽  
Parameter Adjustment ◽  
Inertial Unit

scholarly journals An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds

2018 ◽  
Vol 7 (10) ◽  
pp. 409 ◽  
Author(s):  
Youqiang Dong ◽  
Ximin Cui ◽  
Li Zhang ◽  
Haibin Ai
Keyword(s):  
Point Clouds ◽  
High Density ◽  
Type I ◽  
Densification Process ◽  
Type I Errors ◽  
Second Stage ◽  
Standard Variance ◽  
Triangular Irregular Network ◽  
Two Stages ◽  
Type Ii Errors

The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance.

scholarly journals Allan variance in dynamically tuned inertial-unit gyro error analysis

2019 ◽  
pp. 69-77
Author(s):  
M. A. Sharova ◽  
S. S. Diadin
Keyword(s):  
Angular Velocity ◽  
Error Analysis ◽  
Output Signal ◽  
Allan Variance ◽  
Measurement Information ◽  
Noise Component ◽  
Velocity Sensor ◽  
Signal Noise ◽  
Variance Method ◽  
Inertial Unit

The purpose of the study was to consider an algorithm for obtaining the measurement information from a dynamically tuned gyroscope in the mode of an angular velocity sensor and output signal noise component estimate, the algorithm being based on the Allan variance method. The results obtained were evaluated

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