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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: [math] Calculating gain matrix in KalmanFilter
Date Mon, 04 Aug 2014 19:10:51 GMT
Arne,

I think you are correct.




On Mon, Aug 4, 2014 at 7:34 AM, Arne Schwarz <schwarz.arne@gmail.com> wrote:

> 2014-08-04 13:43 GMT+02:00 Gilles <gilles@harfang.homelinux.org>:
> > On Sun, 3 Aug 2014 18:18:24 +0200, Arne Schwarz wrote:
> >>
> >> Hi,
> >>
> >> I saw that to calculate the gain matrix the actual inverse of the
> >> residual covariance matrix is calculated. Wouldn't it be faster to use
> >> for example a Cholesky decomposition to solve the linear system? Since
> >> a covariance matrix is always symmetric and at least positive
> >> semi-definite.
> >
> >
> > Reading the code (in class "MatrixUtils"), it looks like QR decomposition
> > is used; any problem with that choice?
> >
> > Regards,
> > Gilles
> >
> >
> >
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>
> What I meant was these lines in class "KalmanFilter":
>
> 363        // invert S
> 364        RealMatrix invertedS = MatrixUtils.inverse(s);
> 365
> 366        // Inn = z(k) - H * xHat(k)-
> 367        RealVector innovation =
> z.subtract(measurementMatrix.operate(stateEstimation));
> 368
> 369        // calculate gain matrix
> 370        // K(k) = P(k)- * H' * (H * P(k)- * H' + R)^-1
> 371        // K(k) = P(k)- * H' * S^-1
> 372        RealMatrix kalmanGain =
> errorCovariance.multiply(measurementMatrixT).multiply(invertedS);
>
> I thought it would be better to directly solve the system an not
> calculate the inverse separately, but I may be wrong.
>
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>

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