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I've recorded acceleration signal data using an accelerometer attached to vehicle and I want to know what the differences between the

  • mean filter,
  • trend(mean) removal,
  • baseline correction filter, and
  • filter to correct displacement for oscillation about zero baseline

are.

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A mean filter is one precise type of linear filters that replaces a value by a weighted combination averaging the neighboring values. A constant offset value, or an affine trend, is "quite" invariant by an averaging filter. Therefore, it may estimates a constant or linear baseline well.

Trend removal might be less well-defined. For the mean, there are many alternatives, like the exponential weighted moving average (EWMA), which is an IIR filter.

Baseline is even less well-defined. It can dwell between simple instrumental offets and complicated "references" that may include noie.

The last one is the fuzziest to me. But there is a notion of oscillation, that could be incorporated into a filtering model with knowledge of the signal. Tracking and informed filtering (eg Kalman) can be useful for that purpose

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