Not sure if missing values is the right name to use here. I want to train a DNN on data given by a sensor. The sensor gives the (x,y) coordinates of the founded objects. The sensor can keep track of up to 32 objects at once. If the sensor can't find 32 objects (which is always the case) it sets the x and y coordinates of the objects not found to zero. Could there be a problem training a neural network on this kind of data? The sensor is set on a car, the objects are other cars and the networks job is to predict the next move.
Another problem is that when the sensor finds a new object, already existing/found object might change id. Any tips on that? I am thinking about making randomly permute the indices of the object since that should not make any difference?
Are there any standard solutions to these kind of problems? Especially Setting the distance of non existing objects to zero.