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My thoughts with this actually went more in a process-after-extracting-cluster type algorithm for KNN in order to accurately interpolate what value to shoot at given a set of adjacent-in-n-space values. I think it would be much better than weighting scans by 1/distance or whatever other weighting scheme gets used, as the noise could be eliminated based on location instead of distance and only the trends would be chosen, much like how a histogram allows one to select the highest peak rather than just taking the mean of all the scans. I think it would require fairly large clusters (200 or so points at least), but it could net fairly large gains against the right data patterns.