Hi!, I'm using IMUs for my final project, and I've been studying Kalman filters implementations for a while.
Studying Lauszus code, I see that Kalman filter gain (K0 and K1) is obtained from the error covariance matrix (P00, P01, P10 and P11) multiplied by the time between readings (dt) and added some constants.
If I see how Kalman gains evolve in time, I see that K0 approachs to 1 and, if time between readings remain constant, K1 approachs to -1/dt. If this is OK, where does Kalman filter improve the Complementary Filter?
Thanks and greetings from Argentina