User`s guide

Kalman Filtering
9-61
The time-varying filter also estimates the covariance errcov of the estimatio n
error at each sample. Plot it to see if your filter reached steady state (as
you expect with stationary input noise).
subplot(211)
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 t he same performance as the steady-state version.
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