design patterns - Outlier removal before or after Kalman filtering? -

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I am getting radar data points (X, Y), my position is relative to each MS coordinate system [approx. 10 -15 data points] Now, to guess the better positioning of the points, I would like to apply the Klein filter.

I want to apply the Hyacpass filter further for data in the frequency domain. Which steps are appropriate for applying pen filtering (before or after the outer removal and hyacope filtering)?

Thank you very much for your feedback and please tell me if more information is needed.

PS: I am planning to implement Seminar Clustering for detection of outliners.

If outliers have no information (they are known for bad readings), It would be best to remove them before filter. You can also remove them inside the filter if the y [i] is more than some threshold if the outliners have some information, but the high noise is known, then with the actual high variance of that particular measurement You can reflect by adjusting R . This will have less effect on the result of that measure.

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