What is the Unscented Kalman Filter and when is it used in preference to other types of filters?

edit: I find the Wikipedia explanation a bit too technical to be readily understood.


The Unscented Kalman Filter is a type of non linear Kalman filter. (ie when the transition and observation functions are non linear) If these functions are differentiable, one can simply use the Extended Kalman Filter (EKF). But when the functions are highly non linear, one might need to use an Unscented Kalman Filter (UKF), which is based on the Unscented transform.

The original paper introducing UKF is here.

Modify your question if you have something more precise to ask. What don't you understand in the UKF?


An Unscented Kalman Filer is one of the versions of nonlinear Kalman filter ( together with Extended KF). They solve problems that are non-linear in nature. While the Extended KF uses gradual expansion of linear algorithm, Unscented KF take a unique approach to eliminate linearisation proces [Kim 2011].

The unscented KF does not use the Jacobian to obtain a linear model and so is free from the divergence which can happen with the Extended KF. Downside is that an algorithm can be seen as harder to understand, although it is in the main frame of KF - prediction of state variables based on a system model and calibrating the prediction with measurement to get the final estimate.

The Unscented KF is based mainly on unscented transformation which in its idea is similar to Monte Carlo, but makes a delicate selection of weights for each sample.

References: Kim, P. Huh, L. "Kalman Filter for Beginners: With MATLAB Examples" 2011 http://books.google.co.uk/books?id=W8u_XwAACAAJ

  • $\begingroup$ I honestly find it easier to understand than the EKF. It's derivative-free, doesn't throw gazillions of matrices in your face and just works. :) That said, a very good book on basically all sorts of KFs is Probabilistic Robotics by Thrun et. al. $\endgroup$ – sunside Mar 14 '15 at 4:16

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