As of R2018b we only support a detection-to-track workflow using our trackers, not track-to-track fusers.
By default, the trackers calculate the cost of assigning detections to tracks using the distance method of the track filter. You can bypass it and supply the cost matrix as part of the input if you set the tracker’s HasCostMatrixInput to true.
Assignment is done using the assignment algorithm of your choice. You can also use a custom algorithm if you want.
Once detections are assigned to track (up to one per sensor, but as many as the number of sensors), the track will be updated with the detections. Every detection carries its timestamp. The track will be predicted to the time of the first detection, corrected with it, then predicted to the next detection, corrected, etc., until all the detections are fused. The fusion itself is done using the tracking filter that you choose to use.
There is a metrics object to calculate the tracking accuracy, which is a counterpart of the track assignment metrics object. The name of that object is:trackErrorMetrics.
You can see an example of the outcome of the track error metrices inthis example. If you open the example, you can click on the helperRunTracker function to see it in use.
You can see another example of it being used inthis example. Look for the lines of code in the section Track Using an EKF in Cartesian Coordinates
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