function

Estimation.MocapExternalOdometryErrorState.discreteProcessCovariance

Discrete tangent-space process covariance

Public declarations

Explicit source declarations; dimensions, modifications and defaults are shown as written, without evaluation.

Role	Name	Type	Description
input	`dt`	`Real`	Prediction interval [s]
input	`processNoise`	`ProcessNoise`
output	`covariance`	`Covariance`

Modelica declarations and implementation

Show parameters, interfaces and source

Parameters, inputs, outputs, defaults and equations from the source file.

function discreteProcessCovariance
  "Discrete tangent-space process covariance"
  input Real dt "Prediction interval [s]";
  input ProcessNoise processNoise;
  output Covariance covariance;
algorithm
  covariance := zeros(TangentLength, TangentLength);
  for axis in 1:3 loop
    covariance[axis, axis] := processNoise.attitude * dt;
    covariance[axis + 3, axis + 3] := processNoise.velocity * dt;
    covariance[axis + 6, axis + 6] := processNoise.position * dt;
    covariance[axis + 9, axis + 9] := processNoise.angularVelocity * dt;
  end for;
end discreteProcessCovariance;