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Learning the Unscented Kalman Filter

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Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system.

The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF.

For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and also to simplify the assumption on nonlinear maps.

The code is heavily commented with an example to use the function. Hence, it is sutiable for beginners to learn the UKF. For comparison, the EKF code can be found from http://www.mathworks.com/matlabcentral/fil...objectType=FILE

Free download from Shareware Connection - Nonlinear state estimation is a challenge problem

Publisher: Yi Cao | License: Shareware
Version: 1.0 | Size: 10 KB | Platform: Matlab, Scripts
Released Date: 07-02-2013 | Rating: 0 | Title: Learning the Unscented Kalman Filter

Author Url: http://www.mathworks.com
Program Info Url: http://www.mathworks.com
Download Url: http://www.mathworks.com/matlabcentral/fx_files/18217/3/ukf.zip

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Learning the Unscented Kalman Filter keywords:
Learning the Unscented Kalman Filter related downloads:

Neural Network training using the Unscented Kalman Filter - Similar to using the extended Kalman filter, Neural Networks can also be trained through parameter estimation using the unscented Kalman filter.

Nonlinear least square optimization through parameter estimation using the Unscented Kalman Filter - The Kalman filter can be interpreted as a feedback approach to minimize the least equare error

Neural Network training using the Extended Kalman Filter - The extended Kalman filter can not only estimate states of nonlinear dynamic systems from noisy measurements but also can be used to estimate parameters of a nonlinear system

Kalman Filter Tutorial - This is a simple demo of a Kalman filter for a sinus wave

Unconstrained Optimization using the Extended Kalman Filter - The Kalman filter is actually a feedback approach to minimize the estimation error in terms of sum of square

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