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to understand why we want to use skating you can imagine that we are planning to use a PDF filter when the number of measurements MK is very large for instance suppose you have an amazing sensor that detects the objects with a high probability that has a fairly small clutter intensity and that has an enormous field of view under these conditions we may expect the PDF filter to perform well but if the field of view is sufficiently big we may receive a very large number of clutter detections at every time instance to implement a PDF filter we should compute the posterior mean and covariance which means that we should compute these summations over all measurements which could be computationally demanding if MK is sufficiently large however for the measurements that are far from the predicted measurements the weights are practically zero which means that they do not contribute much to the posterior mean and covariance to save computations we would like to avoid computing the weights means
