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.

  • Constructor Details

    • PreliminaryResult

      protected PreliminaryResult()