Question

Why the kalman filter is better than the other filters?

Answer 1

The main advantage of the Kalman filter is its ability to provide the quality of the estimate (i.e., the variance), and its relatively low complexity. However, its main disadvantage is that it provides accurate results only for Gaussian and linear models. For Gaussian models with limited nonlinearity, extended Kalman filter (EKF) is appropriate. For non-Gaussian and non-linear models, particle filtering (PF) is the most appropriate approach, since it is able to provide arbitrarily posterior probability distribution.

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