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
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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.
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How exactly do I tell if the innovations is uncorrelated?
- ta
wrote

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ta wrote:

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

Tim Wescott
Wescott Design Services
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You could study the output of the estimator(variance and autocorrelation). The variance should be small and the noise white.
Niclas

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