Package com.irurueta.navigation.inertial.calibration.magnetometer

Class RobustKnownHardIronMagneticFluxDensityNormMagnetometerCalibrator.PreliminaryResult

java.lang.Object
com.irurueta.navigation.inertial.calibration.magnetometer.RobustKnownHardIronMagneticFluxDensityNormMagnetometerCalibrator.PreliminaryResult
Enclosing class:
RobustKnownHardIronMagneticFluxDensityNormMagnetometerCalibrator
protected static class RobustKnownHardIronMagneticFluxDensityNormMagnetometerCalibrator.PreliminaryResult extends Object
Internal class containing estimated preliminary result.

  • Field Details

    • estimatedMm

      private com.irurueta.algebra.Matrix estimatedMm
      Estimated magnetometer soft-iron matrix containing scale factors and cross coupling errors. This is the product of matrix Tm containing cross coupling errors and Km containing scaling factors. So tat: 
           Mm = [sx    mxy  mxz] = Tm*Km
          [myx   sy   myz]
          [mzx   mzy  sz ]
      Where: 
           Km = [sx 0   0 ]
          [0  sy  0 ]
          [0  0   sz]
      and 
           Tm = [1          -alphaXy    alphaXz ]
          [alphaYx    1           -alphaYz]
          [-alphaZx   alphaZy     1       ]
      Hence: 
           Mm = [sx    mxy  mxz] = Tm*Km =  [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 accelerometer z-axis is assumed to be the same as the body z-axis. When this is assumed, myx = mzx = mzy = 0 and the Mm matrix becomes upper diagonal: 
           Mm = [sx    mxy  mxz]
          [0     sy   myz]
          [0     0    sz ]
      Values of this matrix are unit-less.

    • covariance

      private com.irurueta.algebra.Matrix covariance
      Covariance matrix.

    • estimatedMse

      private double estimatedMse
      Estimated Mean Squared Error (MSE).

    • 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()