Specifications

10 Design Case Studies
10-60
plot(t,errcov), ylabel('Error covar')
From this covariance plot, you can see that the output covariance did indeed
reach a steady state in about five samples. From then on, your time-varying
filter has the same performance as the steady-state version.
Compare with the estimation error covariance derived from the experimental
data. Type
EstErr = y-ye;
EstErrCov = sum(EstErr.*EstErr)/length(EstErr)
EstErrCov =
0.2718
This value is smaller than the theoretical value errcov and close to the value
obtained for the steady-state design.
Finally, note that the final value and the steady-state value of the
innovation gain matrix coincide.
Mn, M
Mn[] M