Create and initialize a 2-D constant-acceleration linear Kalman filter object from an initial detection report.

Create the detection report from an initial 2-D measurement, (10,−5), of the object position. Assume uncorrelated measurement noise.

detection = objectDetection(0,[10;-5],'MeasurementNoise',eye(2),...'SensorIndex',1,'ObjectClassID',1,'ObjectAttributes',{'Car',5});

Create the new filter from the detection report.

filter = initcakf(detection);

Show the filter state.

filter.State

ans =6×110 0 0 -5 0 0

Show the state transition model.

filter.StateTransitionModel

ans =6×61.0000 1.0000 0.5000 0 0 0 0 1.0000 1.0000 0 0 0 0 0 1.0000 0 0 0 0 0 0 1.0000 1.0000 0.5000 0 0 0 0 1.0000 1.0000 0 0 0 0 0 1.0000