Online Estimation of Allan Variance Parameters

2000 ◽  
Vol 23 (6) ◽  
pp. 980-987 ◽  
Author(s):  
Jason J. Ford ◽  
Michael E. Evans
Keyword(s):  
Allan Variance ◽  
Online Estimation ◽  
Variance Parameters

scholarly journals Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

Sensors ◽  
2015 ◽  
Vol 15 (2) ◽  
pp. 2496-2524 ◽  
Author(s):  
Zhiyong Miao ◽  
Feng Shen ◽  
Dingjie Xu ◽  
Kunpeng He ◽  
Chunmiao Tian
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Allan Variance ◽  
Online Estimation

scholarly journals Online Estimation of ARW Coefficient of Fiber Optic Gyro

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yang Li ◽  
Baiqing Hu ◽  
Fangjun Qin ◽  
Kailong Li
Keyword(s):  
Kalman Filter ◽  
Fiber Optic ◽  
Noise Analysis ◽  
Digital Simulation ◽  
State Space Model ◽  
Allan Variance ◽  
Measurement Noise ◽  
Fiber Optic Gyro ◽  
Online Estimation ◽  
Measurement Noise Covariance

As a standard method for noise analysis of fiber optic gyro (FOG), Allan variance has too large offline computational burden and data storages to be applied to online estimation. To overcome the barriers, the state space model is firstly established for FOG. Then the Sage-husa adaptive Kalman filter (SHAKF) is introduced in this field. Through recursive calculation of measurement noise covariance matrix, SHAKF can avoid the storage of large amounts of history data. However, the precision and stability of this method are still the primary matters that needed to be addressed. Based on this point, a new online method for estimation of the coefficient of angular random walk is proposed. In the method, estimator of measurement noise is constructed by the recursive form of Allan variance at the shortest sampling time. Then the estimator is embedded into the SHAKF framework resulting in a new adaptive filter. The estimations of measurement noise variance and Kalman filter are independent of each other in this method. Therefore, it can address the problem of filtering divergence and precision degrading effectively. Test results of both digital simulation and experimental data of FOG verify the validity and feasibility of the proposed method.

Online estimation method of Allan variance coefficients for MEMS IMU

2014 ◽  
Vol 9 (09) ◽  
pp. P09001-P09001 ◽  
Author(s):  
Z Y Miao ◽  
F Shen ◽  
D J Xu ◽  
C M Tian ◽  
K P He
Keyword(s):  
Estimation Method ◽  
Allan Variance ◽  
Online Estimation ◽  
Mems Imu

Translocation Relative to Range

Methodology ◽  
2008 ◽  
Vol 4 (3) ◽  
pp. 132-138 ◽  
Author(s):  
Michael Höfler
Keyword(s):  
Risk Difference ◽  
Binary Outcomes ◽  
Number Needed To Treat ◽  
Likert Scales ◽  
Psychological Science ◽  
The Absolute ◽  
The Difference ◽  
Variance Parameters ◽  
Standardized Index ◽  
Maximum Possible Magnitude

A standardized index for effect intensity, the translocation relative to range (TRR), is discussed. TRR is defined as the difference between the expectations of an outcome under two conditions (the absolute increment) divided by the maximum possible amount for that difference. TRR measures the shift caused by a factor relative to the maximum possible magnitude of that shift. For binary outcomes, TRR simply equals the risk difference, also known as the inverse number needed to treat. TRR ranges from –1 to 1 but is – unlike a correlation coefficient – a measure for effect intensity, because it does not rely on variance parameters in a certain population as do effect size measures (e.g., correlations, Cohen’s d). However, the use of TRR is restricted on outcomes with fixed and meaningful endpoints given, for instance, for meaningful psychological questionnaires or Likert scales. The use of TRR vs. Cohen’s d is illustrated with three examples from Psychological Science 2006 (issues 5 through 8). It is argued that, whenever TRR applies, it should complement Cohen’s d to avoid the problems related to the latter. In any case, the absolute increment should complement d.

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