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Kalman Filters & accelerometers - linear vs extended, what do I need?

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Jun1-09, 12:53 PM
P: 19
I have a problem for work where I am attempting to integrate (a single) accelerometer readout in order to gain insight into the resulting velocity and position curves with respect to time. From experience I know this is a tricky task due to drift and error being amplified when integrated.

Just some background: displacement of our subject will most likely occur over a short time span - millisecond scale due to an impact force during the test. A LDVT will also be implemented during the test in an attempt to directly measure displacement with time. However, I believe this data will be erroneous and spotty at best due to the high sample rates involved (possibly tens of khz) Worst case, the LVDT will give us a very accurate total displacement value with which we can check our accelerometer against.

Based on my reading and intermediate understanding of the linear Kalman filter, I am suspecting that I will require an extended Kalman filter (EKF) in order to model the nonlinear accelerometer data and LVDT displacement curve.

Since both of these tools can eventually measure the same thing (displacement) it is my thinking that their results can be combined and filtered to eventually wind up with a displacement curve w.r.t time that agrees with the total displacement measured by the LVDT. Please see this flow chart:

[Accelerometer data] + [EKF] -> [Double integration] --> [Displacement curve 1]

[LVDT] + [EKF] --> [Displacement curve 2]

EKF {[Displacement curve 1] + [Displacement curve 2]} --> [Most trustworthy displacement curve]

I want to figure out from those with more experience if this is feasible and if I am on the right track..? Thank you for reading and I appreciate any tips/criticism or suggested reading you may offer.
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