| Types.Init | init (from PartialDiscreteBlock) | Modelica_LinearSystems2.Controllers.Internal.convertToInit(initType, sampleClock.initType) | Type of initialization (NoInit/SteadyState/InitialState/InitialOutput) |
| Integer | sampleFactor (from PartialDiscreteBlock) | 1 | Sample factor (Ts = sampleFactor * sampleClock.sampleTime) |
| Modelica.Units.SI.Time | Ts (from PartialDiscreteBlock) | sampleClock.sampleTime*sampleFactor | Sample time |
| Real[:] | x_est_init | {0, 0, 0, 0} | Initial value for state estimation |
| Real[:,size(Q, 1)] | Q | identity(size(x_est_init, 1)) | Covariance matrix of the associated process noise |
| Real[size(x_est_init, 1),size(Q, 1)] | G | | Process noise weight matrix (usually identity or input matrix) |
| Real[size(x_est_init, 1),size(x_est_init, 1)] | Q2 | G*Q*transpose(G) | Appropriately weighted process noise covariance matrix |
| Real[:,size(R, 1)] | R | | Covariance matrix of the measurement noise |
| Real[nx,nx] | CfQ | MatricesMSL.cholesky(Q2, false) | Left Cholesky factor of the weighted noise covariance Matrix G*Q*G' |
| Real[ny,ny] | CfR | MatricesMSL.cholesky(R, false) | Confidence of measurements - large values low confidence | acts like coefficient of PT1-Filter |
| Real[:,:] | P_init | identity(size(x_est_init, 1)) | Initial value of the error covariance matrix |
| Integer | nu | 1 | Number of system inputs |
| Real | alpha | 0.1 | Spread of sigma points |
| Real | beta | 2 | Characteristic of the distribution of x |
| Real | kappa | 0 | Scaling kurtosis of sigma point distribution |
| Integer | nx | size(x_est_init, 1) | Number of system states |
| Integer | ny | size(R, 1) | Number of observed measurements |
| Advanced options |
| Types.InitWithGlobalDefault | initType (from PartialDiscreteBlock) | Types.InitWithGlobalDefault.UseSampleClockOption | Type of initialization (NoInit/SteadyState/InitialState/InitialOutput) |
