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My project:

I'm developing a slot car with 3-axis accelerometer and gyroscope, trying to estimate the car pose (x, y, z, yaw, pitch) but I have a big problem with my vibration noise (while the car is running, the gears induce vibration and the track also gets it worse) because the noise takes values between ±4[g] (where g = 9.81 [m/s^2]) for the accelerometers, for example.

I know (because I observe it), the noise is correlated for all of my sensors

In my first attempt, I tried to work it out with a Kalman filter, but it didn't work because values of my state vectors had a really big noise.

EDIT2: In my second attempt I tried a low pass filter before the Kalman filter, but it only slowed down my system and didn't filter the low components of the noise. At this point I realized this noise might be composed of low and high frecuency components.

I was learning about adaptive filters (LMS and RLS) but I realized I don't have a noise signal and if I use one accelerometer signal to filter other axis' accelerometer, I don't get absolute values, so It doesn't work.

EDIT: I'm having problems trying to find some example code for adaptive filters. If anyone knows about something similar, I will be very thankful.

Here is my question:

Does anyone know about a filter or have any idea about how I could fix it and filter my signals correctly?

Thank you so much in advance,

XNor

PD: I apologize for any mistake I could have, english is not my mother tongue

XNor
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3 Answers3

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The first thing i would do, would be to run a DFT on the sensor signal and see if there is actually a high and low frequency component of your accelerometer signals.

With a DFT you should be able to determine an optimum cutoff frequency of your lowpass/bandpass filter.

If you have a constant component on the Z axis, there is a chance that you haven't filtered out gravity. Note that if there is a significant pitch or roll this constant can be seen on your X and Y axes as well

Generally pose estimation with an accelerometer is not a good idea as you need to integrate the acceleration signals twice to get a pose. If the signal is noisy you are going to be in trouble already after a couple of seconds if the noise is not 100% evenly distributed between + and -.

If we assume that there is no noise coming from your gears, even the conversion accuracy of the Accelerometer might start to mess up your pose after a couple of minutes.

I would definately use a second sensor, eg a compass/encoder in combination with your mathematical model and combine all your sensor data in a kalmann filter(Sensor fusion).

You might also be able to derive a black box model of your noise by assuming that it is correlated with your motors RPM. (Box-jenkins/Arma/Arima).

Sigurd V
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  • Hello Sigurd, thank you for your answer. I decided to replan my pose estimation, because It turned out the noise wasn't correlated (but it seemed like). I have an encoder and I think with a particle filter using the track geometry as a rescritcion and read tags for absolute position could work, but I don't feel quite confident with this model, so I'll read about this black box (Box-jenkins/Arma/Arima) and try the kalman filter only measuring speed (linear and angular) and use theese to propagate my particle filter, and update it with the track restrictions and tags. – XNor Apr 08 '13 at 09:29
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I had similar problems with noise with low and high frequencies and I managed to decently remove it without removing good signal too by using an universal microphone shock mount. It does a good job with gyroscope too especially if you find one which fits it (or you can put it in a small case then mount it) It basically uses elastic strings to remove shocks and vibration.Universal shock mount

Botirla Sorin
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Have you tried a simple low-pass filter on the data? I'd guess that the vibration frequency is much higher than the frequencies in normal car acceleration data. At least in normal driving. Crashes might be another story...

japreiss
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  • Hi japreiss, thank you for your answer. Yes, I have tried a normal low pass filter, that's how I realized they're correlated because it is high frecuency noise, but still implementing it, it turned out it is not enough because it slows down my system and does not filter the low components of the noise, that's why I thought about adaptive filters. – XNor Apr 02 '13 at 14:22