An extension of sigma-point Kalman filtering using nonlinear estimator bases

Authors:
Timothy J. Wheeler;Andrew K. Packard
Affiliations:
Department of Mechanical Engineering, University of California, Berkeley;Department of Mechanical Engineering, University of California, Berkeley
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

Citing 2
Cited 0

Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion

Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

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Abstract

This paper investigates the problem of state estimation for nonlinear discrete-time dynamic systems. The estimator is parameterized as a linear combination of chosen basis functions. We seek the parameter that minimizes the mean squared estimation error (MSE); however, computing this objective is intractable. Hence, the MSE is approximated using the Scaled Unscented Transform (SUT), which yields a discrete least-squares optimization problem. Tikhonov regularization is used to avoid overfitting the data supplied by the SUT. A double pendulum example is used to compare this estimation strategy to the Unscented Kalman Filter.