I have these two functions when I use "Sensor Fusion and Tracking Toolbox" or "Automated Driving Toolbox", but sometimes I don't know which function is better to use. It is easy to confuse. For example, why is the result different in the following example?
detection = objectDetection(0,[-250;-40;0],'MeasurementNoise',2.0*eye(3), ...
'SensorIndex',1,'ObjectClassID',1,'ObjectAttributes',{'Car',2});
filter = initctekf(detection)
filter =
trackingEKF with properties:
State: [7×1 double]
StateCovariance: [7×7 double]
StateTransitionFcn: @constturn
StateTransitionJacobianFcn: @constturnjac
ProcessNoise: [4×4 double]
HasAdditiveProcessNoise: 0
MeasurementFcn: @ctmeas
MeasurementJacobianFcn: @ctmeasjac
MeasurementNoise: [3×3 double]
HasAdditiveMeasurementNoise: 1
Why is the "state" dimension 7 and what do they mean?
Another "equivalent" is: ?
EKF = trackingEKF(@constturn,@ctmeas,[-250;-40;0], ...
'MeasurementNoise',2.0*eye(3),...
'StateTransitionJacobianFcn',@constveljac, ...
'MeasurementJacobianFcn',@cvmeasjac)
EKF =
trackingEKF with properties:
State: [3×1 double]
StateCovariance: [3×3 double]
StateTransitionFcn: @constturn
StateTransitionJacobianFcn: @constveljac
ProcessNoise: [3×3 double]
HasAdditiveProcessNoise: 1
MeasurementFcn: @ctmeas
MeasurementJacobianFcn: @cvmeasjac
MeasurementNoise: [3×3 double]
HasAdditiveMeasurementNoise: 1
Another question is why the dimensions of the parameters obtained by the two ways of writing are different? It is not easy to find the answer description from the help document
Best Answer