trackingKF does not output Kalman Gain

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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

Accepted Answer

Prashant Arora
Prashant Arora on 15 Nov 2023
Edited: Prashant Arora on 21 Nov 2023
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 Comments
Christian Busse
Christian Busse on 20 Nov 2023
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 on 21 Nov 2023
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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