You mention that your measurement function already knows how to handle different sensor models based on SensorIndex. So, the tracker should be able to do the right thing without any additional work for you.
In other words, given a measurement function h of the following format:
function zexp = h(state, measurementParameter)
% measurementParameters are supplied to the function by the tracker from
% the objectDetection.MeasurementParameters property.
if measurementParameters.SensorInex == 1 % Let's say 1 is vision model
zexp = hvision(state, measurementParameters);
else % Use radar model
zexp = hradar(state, measurementParameters);
end
The hvision and hradar measurement function should allow you to convert from the state in global coordinates to the expected measurement in vision and radar measurement space, respectively. The measurementParameters passed to those functions will define things like where the sensor is mounted, how it is oriented, etc. These are defined in the objectDetection as defined in https://www.mathworks.com/help/fusion/ug/convert-detections-into-objectDetection-format.html
The trackers will assign each sensor separately using the SensorIndex property in the objectDetections. Cost calculation will use the expected measurement from the function above.
Note that cost is:
(z-h(x))' / (R+HPH') * (z-h(x)) + log(det(R+HPH')).
The first part is Mahalonobis distance while the log(det()) part is a compensation to avoid assignment of tracks with large uncertainty (which causes HPH' to be large).
The tracker uses the measurement functions to update the track state in global coordinates. You don't need to do anything else other than defining the measurement functions correctly.
