trackingKF does not output Kalman Gain

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Christian Busse
Christian Busse 2021-9-17
评论： Prashant Arora 2023-11-21
Hello,
why does trackingKF do not output the Kalman Gain?
Also, I think that an option to specify 'time-varying' or 'time-invariant' KF would be useful.
Best Regards
Christian

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Prashant Arora
Prashant Arora 2023-11-15
编辑：Prashant Arora 2023-11-21
Hi,
As of R2024a, trackingKF does not output Kalman gain. We will consider this enhancement in the future releases.
As a workaround, you can compute Kalman gain using the output of the residual method.
predict(kf);
[r, S] = residual(kf, z);
H = kf.MeasurementModel;
P = kf.StateCovariance;
kalmanGain = P*H'/S;
Hope this helps.
Prashant
  2 个评论
Christian Busse
Christian Busse 2023-11-20
Hello, thanks for the response.
My workaround looks similar:
S = H*P*H' + R;
B = H*P';
K = (S \ B)';
Do you think both approaches are equal or maybe the other is numerically more robust/efficient in MATLAB?
Prashant Arora
Prashant Arora 2023-11-21
Hi Christian,
Your approach looks equivalent to me.
You could consider modifying it a bit to reduce one transpose operation. However, I don't believe its going to have any noticeable impact on performance.
S = H*P*H' + R;
B = P*H';
K = B/S;
The only reason to use "residual" method would be to extend the same method to other Gaussian filters such as trackingEKF and trackingUKF. Internally, trackingEKF and trackingUKF also use numerically robust approach to compute the "S" (innovation covariance) using square root implementation. However, trackingKF is still plain vanilla (no square root) as of R2024a. So, your workaround looks equivalent trackingKF. We'll consider enhancing trackingKF in a future release to also use square-root implementation.
Hope this helps.

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