> If the quality of the measurements are high enough, and the sampling frequency is high enough. Then the Kalman filter should be able to converge to a very goo

> If the quality of the measurements are high enough, and the sampling frequency is high enough. Then the Kalman filter should be able to converge to a very good estimate of state, even with no motion model right? No. Consider this discrete-time linear system with 2-dimensional state [x1, x2] and one output y: x1' = 2 * x1 x2' = x2 y = x1 + x2 Suppose we see the output sequence 1, 2, 4, 8, ... If we know the motion model, then we know that the initial state must have been [1, 0]. (In technical terms, the pair (A, C) is observable.) But if we don't have the motion model -- all we know is that y = x1 + x2 -- there's no hope of ever getting a state estimate, even if our y measurement has zero noise.