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Kalman tuning

Hi,

I am a newbie in need of help tuning a Kalman filter. The tuning knobs I have are the process noise of the filter. I have measurements statistics characterized by the sensor manufacturer and I am using that as the measurement noise. Other than the measurements, I do not have any other truth data. So how do I tell if the tuning is resulting in a better filter or not. In a crude sense, I can tell a bad filter if the filter output diverges from the measurements. But other than that, how do I tell if the filter output is converging to the truth. How do I tell if one set of process noise figures are better than another?

Thanks,

- ta

Reply to
ta
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"ta" wrote

Look at the innovations (the error) of the Kalman filter. If it's white (uncorrelated), then that's one indication that the filter is working well.

Ciao,

Peter K.

Reply to
Peter J. Kootsookos

You could study the output of the estimator(variance and autocorrelation). The variance should be small and the noise white.

Reply to
Niclas Granqvist

How exactly do I tell if the innovations is uncorrelated?

- ta

Reply to
ta

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Correlate them, and see if the answer averages to zero.

Reply to
Tim Wescott

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