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Robotics

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  • $\begingroup$ That's actually one of the approachs I had in mind, however with multiple targets I would need a way to associate each sample to the "correct target" (data association) without restrains on the distance between targets. Notice that the targets do not have unique IDs. $\endgroup$ Commented Jan 25, 2016 at 16:10
  • $\begingroup$ @nVolteX - If the targets don't have unique IDs, then how are you comparing subsequent measurements between samples to determine that the position measurements are noisy? If your problem is needing to know how to associate measurements to a particular target, then you should ask it as a new question and link to this one to give context. $\endgroup$ Commented Jan 25, 2016 at 16:49
  • $\begingroup$ The problem is multi-target tracking/position estimation, which in order to be solved needs to go through some kind of "data association" and tracking/filtering. For example, if I could assume to have information about the number of targets then something like the k-means clustering would solve both problems. However the "hard" part, comes when I don't have prior information about the number of targets. $\endgroup$ Commented Jan 25, 2016 at 18:27