On 10 Sep, 10:45, Kai <inva...@invalid.invalid> wrote:
> Hello,
>
> I'm using a Kalman-filter for state estimation issues. My question is
> dealing with the signals that I apply to the filter.
>
> Let's think of an analog sensor with a changeable low-pass filter at its
> output.
Are you talking about simulations? If so, why deal with analog
models?
Or are you talking about anti-alias filters?
> I could think of tuning the RC-elements to a bandwith of let's say
> 40 Hz or 400 Hz. When I measure the noise levels and variances I can use
> these values in both cases to feed my Kalman-filter with the measurement
> covariance-matrices.
You can simulate this without talking about analog electronics.
> I wonder if there is a trade-off, maybe from a theoretical point-of-view, in
> filtering at an early point my signal with a low frequency and then apply
> it to the filter, or filter with a higher frequency and use the more noisy
> signal in the Kalman-filter with modelling the noise accordingly.
The wider the bandwidth of the pre-filter the more noise reaches
the Kalman filter. I would assume that the amount of noise has an
impact on th eperformance of the *system*. A narrow-band signal
will be relatively insensitive to changes at the input and slow
to react, wherea the wide-band signal will give faster reactions
but maybe a more 'nervous' system.
So you are left with all the usual tradeoffs in system design.
Rune
Reply by Kai●September 10, 20082008-09-10
Hello,
I'm using a Kalman-filter for state estimation issues. My question is
dealing with the signals that I apply to the filter.
Let's think of an analog sensor with a changeable low-pass filter at its
output. I could think of tuning the RC-elements to a bandwith of let's say
40 Hz or 400 Hz. When I measure the noise levels and variances I can use
these values in both cases to feed my Kalman-filter with the measurement
covariance-matrices.
I wonder if there is a trade-off, maybe from a theoretical point-of-view, in
filtering at an early point my signal with a low frequency and then apply
it to the filter, or filter with a higher frequency and use the more noisy
signal in the Kalman-filter with modelling the noise accordingly.
I hope the question becomes clear.
TIA,
Kai