This operator uses a Kalman Filter to produce a statistically optimal estimate of the underlying system state.
Parameter
- Variables: The name of the variables
- Attributes: The attributes to feed the filter
- Transition: The transition matrix 'A'
- ProcessNoise: The process noise matrix 'Q'
- Measurement: The measurement matrix 'H'
- MeasurementNoise: The measurement noise matrix 'R'
- InitialState: The initial state vector 'x' (optional)
- InitialError: The initial error matrix 'P' (optional)
- Control: The control matrix 'B' (optional)
Example
| Code Block | ||||
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out = KALMAN({
VARIABLES = ['x'],
ATTRIBUTES = ['m'],
TRANSITION = '[1.0]',
PROCESSNOISE = '[2.0]',
MEASUREMENT = '[1.0]',
MEASUREMENTNOISE = '[4.0]'},
in) |
| Code Block | ||||||
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out = KALMAN({
VARIABLES = ['x','y','dx','dy'],
ATTRIBUTES = ['vx','vy'],
INITIALSTATE = '[0.0, 0.0, 0.0, 0.0]',
INITIALERROR = '[1.0,0.0,0.0,0.0;0.0,1.0,0.0,0.0;0.0,0.0,1.0,0.0;0.0,0.0,0.0,1.0]',
TRANSITION = '[1.0,0.0,1.0,0.0;0.0,1.0,0.0,1.0;0.0,0.0,1.0,0.0;0.0,0.0,0.0,1.0]',
PROCESSNOISE = '[1/4, 1/4, 1/2, 1/2;1/4, 1/4, 1/2, 1/2; 1/2, 1/2, 1, 1; 1/2, 1/2, 1, 1]',
MEASUREMENT = '[0.0,0.0,1.0,0.0;0.0,0.0,0.0,1.0]',
MEASUREMENTNOISE = '[10.0,0.0;0.0,10.0]'},
in) |