Specifications

Kalman Filtering
10-59
x = A*x + B*u(i); % x[n+1|n]
P = A*P*A' + B*Q*B'; % P[n+1|n]
end
You can now compare the true and estimated output graphically.
subplot(211), plot(t,y,'--',t,ye,'-')
title('Time-varying Kalman filter response')
xlabel('No. of samples'), ylabel('Output')
subplot(212), plot(t,y-yv,'-.',t,y-ye,'-')
xlabel('No. of samples'), ylabel('Output')
Thefirstplot showsthetrue response (dashed line) and the filtered response
(solid line). The second plot compares the measurement error (dash-dot)
with the estimation error (solid).
The time-varying filter also estimates the covariance
errcov of the estimation
error at each sample. Plot it to see if your filter reached steady state (as
you expect with stationary input noise).
subplot(211)
y
y
e
yy
e
–