Hi,
Due to the need to support code generation, the sizes of state, state covariance, process noise, and measurement noise all have to be fixed in the filter.
To be able to support your use case, you can modify your definition of measurement function and measurement noise in the following way:
For the measurement function, define a function that allows measurement parameters to be passed into the function. As one of those measurement parameters pass an ID of the sensor from which the measurement was obtained. For example:
function z = myMeas(x,sensorID,param1,param2)
z = zeros(7,1,'like',x); % The largest measurement size, assuming the class of x is the same as measurement
if sensorID == 1
z(1:4) = firstModel(x,param1,param2); % That's the four-element measurement model
else
z(:) = secondModel(x,param1,param2); % That's the seven-element measurement model
end
end
Similarly, you can create a myMeasJacobian function
Now, when you get measurements from your sensors, before you actually pass them to the filter, you will have to pre-process them to the largest measurement length. For example:
function paddedMeas = padMeasurement(originalMeas,sensorID)
paddedMeas = zeros(7,1,'like',originalMeas);
if sensorID == 1
paddedMeas(1:4) = originalMeas(:);
else
paddedMeas(:) = originalMeas(:);
end
end
A similar function can be written for the measurement noise.
HTH,
Elad
