Package com.irurueta.navigation.inertial.calibration.gyroscope

Class RobustKnownBiasAndFrameGyroscopeCalibrator.PreliminaryResult

java.lang.Object

com.irurueta.navigation.inertial.calibration.gyroscope.RobustKnownBiasAndFrameGyroscopeCalibrator.PreliminaryResult

Enclosing class:

RobustKnownBiasAndFrameGyroscopeCalibrator

protected static class RobustKnownBiasAndFrameGyroscopeCalibrator.PreliminaryResult
extends Object

Internal class containing estimated preliminary result.

Field Summary

Fields

Modifier and Type	Field	Description
private com.irurueta.algebra.Matrix	covariance	Covariance matrix for estimated result.
private double	estimatedChiSq	Estimated chi square value.
private int	estimatedChiSqDegreesOfFreedom	Estimated degrees of freedom of chi square value.
private com.irurueta.algebra.Matrix	estimatedGg	Estimated G-dependent cross biases introduced on the gyroscope by the specific forces sensed by the accelerometer.
private com.irurueta.algebra.Matrix	estimatedMg	Estimated gyroscope scale factors and cross coupling errors.
private double	estimatedMse	Estimated Mean Square Error.
private double	estimatedP	Estimated probability of finding a smaller chi square value expressed as a value between 0.0 and 1.0.
private double	estimatedQ	Estimated measure of quality of estimated fit as a value between 0.0 and 1.0.
private double	estimatedReducedChiSq	Estimated reduced chi square value.

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	PreliminaryResult()

Method Summary

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

estimatedMg

private com.irurueta.algebra.Matrix estimatedMg

Estimated gyroscope scale factors and cross coupling errors. This is the product of matrix Tg containing cross coupling errors and Kg containing scaling factors. So that:

     Mg = [sx    mxy  mxz] = Tg*Kg
          [myx   sy   myz]
          [mzx   mzy  sz ]
 

Where:

     Kg = [sx 0   0 ]
          [0  sy  0 ]
          [0  0   sz]
 

and

     Tg = [1          -alphaXy    alphaXz ]
          [alphaYx    1           -alphaYz]
          [-alphaZx   alphaZy     1       ]
 

Hence:

     Mg = [sx    mxy  mxz] = Tg*Kg =  [sx             -sy * alphaXy   sz * alphaXz ]
          [myx   sy   myz]            [sx * alphaYx   sy              -sz * alphaYz]
          [mzx   mzy  sz ]            [-sx * alphaZx  sy * alphaZy    sz           ]
 

This instance allows any 3x3 matrix however, typically alphaYx, alphaZx and alphaZy are considered to be zero if the gyroscope z-axis is assumed to be the same as the body z-axis. When this is assumed, myx = mzx = mzy = 0 and the Mg matrix becomes upper diagonal:

     Mg = [sx    mxy  mxz]
          [0     sy   myz]
          [0     0    sz ]
 

Values of this matrix are unit-less.

estimatedGg

private com.irurueta.algebra.Matrix estimatedGg

Estimated G-dependent cross biases introduced on the gyroscope by the specific forces sensed by the accelerometer. This instance allows any 3x3 matrix.

covariance

private com.irurueta.algebra.Matrix covariance

Covariance matrix for estimated result.

estimatedMse

private double estimatedMse

Estimated Mean Square Error.

estimatedChiSq

private double estimatedChiSq

Estimated chi square value.

estimatedChiSqDegreesOfFreedom

private int estimatedChiSqDegreesOfFreedom

Estimated degrees of freedom of chi square value. Degrees of freedom is equal to the number of sampled data minus the number of estimated parameters.

estimatedReducedChiSq

private double estimatedReducedChiSq

Estimated reduced chi square value. This is equal to estimated chi square value divided by its degrees of freedom. Ideally this value should be close to 1.0.

estimatedP

private double estimatedP

Estimated probability of finding a smaller chi square value expressed as a value between 0.0 and 1.0. The smaller the found chi square value is, the better the fit of the estimated parameters to the actual parameter. Thus, the smaller the chance of finding a smaller chi square value, then the better the estimated fit is.

estimatedQ

private double estimatedQ

Estimated measure of quality of estimated fit as a value between 0.0 and 1.0. The larger the quality value is, the better the fit that has been estimated.

Constructor Details

PreliminaryResult

protected PreliminaryResult()