Bias/Dirft compensation for integration of linear accelerometer data using Kalman filtering

Question

I have become a part of this infinite question of how to estimate position from accelerometer data achieved by an Inertial measurement unit. I am wondering how to compensate for integration ''drift'' during linear movement using Kalman filtering.

At this moment I got my acceleration in a fixed coordinate system and all movements are in know directions with no change in angular position.

So at this point we got acceleration in 3D (x-y-z) in known directions, an acceleration in x will yield for zero acceleration in y and z and so on. Assuming perfect conditions, which are not the case, of course some noise with be added to the other directions when moving in one direction but lets ''leave'' this out at this point. In addition, It is important to note that the system only has to estimate a limited period, approximately about 1 second using a sampling freq of 512 Hz.

It also important to note that I have compensated for the offset (gravity and misalignment of the accelerometer in the IMU) and bias of the acceleromter data when static. Meaning when the sensor is non-moving all my readings are constant zero before going into the Kalman filter.

To more characterize my problem I have this graph to illustrate my problem with drift. This is estimations on 5 seconds to more show what I'm struggling with. Position-estimation-drift-problem

Here we are looking into a movement in one direction, the movement are 20cm movement in y direction which in my case are forward relative to my starting position.

Is there a way to reduce/eliminate this drift when integrating my signal. For instance assume something about drifting when my sensor is non-moving. Or to compute using some correction in my Kalman algorithm to subtract or add to my estimated velocity and position. The system does not have to run in real time so any tuning bias compensation can be adjusted for looking back into the data. But I would be preferable if it was possible to take new measurements with slightly different movements and not tune more then needed.

Finally where/how can I compensate for this, in the Kalman algorithm or before/after, or should I be in for a disappointment already?

If I left out some important information please ask so i can elaborate more, an at last any thoughts/ideas are welcome!

Remember I do only need to estimate for second’s worth of time so my hope is that this makes it more achievable, but i might be wrong?

Answer 1

I can only guess/suggest few tricks, but you will probably get some significant error if you only based on accelerometer.

1. seems that detecting motionless is not resetting the speed, just acceleration (according to your graph) so this should be an easy fix
2. if we are talking an a car/other type of surface motion with contact / friction, your motionless can be set by characterizing the noise of in motion/self sensor noise
3. kalman parameters may be off
4. run multiple kernels and average results (may also try particle filter)
5. if its not for online application you can also try fitting offsets/drift and reduce them by assuming there is not motion in constant speed or other approaches that can replace the kalman filter which is designed for real time best estimation.
6. error seems a-symmetric in time, just run it in both directions (:
7. what are you measuring at 512 Hz??? maybe you can better model it

I can go on and on but if you supply data and code, it would be much easier.

Good luck, Lev