No, but I am developing one with an extended Kalman filter where i
will test various decompositions. I just stumbled over these lines
because I read somewhere that explicit calculation of the inverse is
not a thing one should do. And I suggested the Cholesky decomposition
because it should be fast on these special matrices. But as I wanted
to write an issue on this subject I saw an old resolved one where
exactly the Cholesky decomposition was mentioned to be unsuitable
because real world covariance matrices may not be perfectly symmetric.
But it still may be better to use the QR decomposition directly
without going over the inverse.
This is the code i wanted to suggest in the issue, on may want to
replace "Cholesky" with "QR":
// calculate gain matrix
// K(k) = P(k) * H' * (H * P(k) * H' + R)^1
// K(k) = P(k) * H' * S^1
// K(k) * S = P(k) * H'
// S' * K(k)' = H * P(k)'
RealMatrix kalmanGain = new CholeskyDecomposition(s).getSolver().solve(
errorCovariance.transpose().multiply(measurementMatrix)).transpose();
20140805 18:13 GMT+02:00 Thomas Neidhart <thomas.neidhart@gmail.com>:
> On 08/04/2014 09:10 PM, Ted Dunning wrote:
>> Arne,
>>
>> I think you are correct.
>
> Afaik, it is possible for unscented Kalman filters to avoid the explicit
> matrix inversion (see also
> http://en.wikipedia.org/wiki/Cholesky_decomposition#Kalman_filters).
>
> We have an open issue which was delayed for 4.0 to refactor the filter
> package and to add various forms of Kalman filters (i.e. extended,
> nonlinear, unscented).
>
> The performance issue should be analyzed within this context.
>
> @Arne: do you have a usecase that demonstrates bad performance which
> could be improved by the use of Cholesky decomposition. This would be
> great and help us a lot.
>
> Thanks,
>
> Thomas
>
>>
>> On Mon, Aug 4, 2014 at 7:34 AM, Arne Schwarz <schwarz.arne@gmail.com> wrote:
>>
>>> 20140804 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
>>>>> semidefinite.
>>>>
>>>>
>>>> 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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