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This operator uses a kalman filter to estimate the distribution of one or more attribute values Kalman Filter to produce a statistically optimal estimate of the underlying system state.

Parameter

  • Variables: The name of the variables
  • Attributes: The attributes to perform feed the filter on
  • 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
languagepql
linenumberstrue
out = KALMAN({MEASUREMENT
              VARIABLES = ['[1.0]',x'], 
              ATTRIBUTES = ['m'],
              TRANSITION = '[1.0]', ProcessNoise 
              PROCESSNOISE = '[2.0]', ATTRIBUTES
              MEASUREMENT = '['x'], 1.0]', 
              MEASUREMENTNOISE = '[4.0]'}, 
             in)
Code Block
themeEclipse
languagepql
linenumberstrue
inout = 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]', MEASUREMENT = '[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]', ATTRIBUTES
             MEASUREMENT = ['x1','x2'],'[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]'}, out
            in)