Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
| Hi-index | 0.00 |
In this paper, we propose the conceptual use of fuzzy clustering techniques as iterative spatial methods to estimate a posteriori statistics in place of the weighted averaging scheme of the Unscented Kalman filter. Specifically, instead of a linearization methodology involving the statistical linear regression of the process and measurement functions through some deterministically chosen set of test points (sigma points) contained within the ''uncertainty region'' around the state estimate, we present a variant of the Unscented transformation involving fuzzy clustering techniques which will be applied to the test points yielding ''degrees of membership'' in which Gaussian shapes can be ''fit'' using a least squares scheme. Implementation into the Kalman methodology will be shown along with simple state and parameter estimation examples.