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Gaussian MAP Filtering Using Kalman Optimization

Angel Garcia-Fernandez (Institutionen för signaler och system, Signalbehandling) ; Lennart Svensson (Institutionen för signaler och system, Signalbehandling)
IEEE Transactions on Automatic Control (0018-9286). Vol. 60 (2015), 5, p. 1336-1349.
[Artikel, refereegranskad vetenskaplig]

This paper deals with the update step of Gaussian MAP filtering. In this framework, we seek a Gaussian approximation to the posterior probability density function (PDF) whose mean is given by the maximum a posteriori (MAP) estimator. We propose two novel optimization algorithms which are quite suitable for finding the MAP estimate although they can also be used to solve general optimization problems. These are based on the design of a sequence of PDFs that become increasingly concentrated around the MAP estimate. The resulting algorithms are referred to as Kalman optimization (KO) methods. We also provide the important relations between these KO methods and their conventional optimization algorithms (COAs) counterparts, i.e., Newton's and Levenberg-Marquardt algorithms. Our simulations indicate that KO methods are more robust than their COA equivalents.

Nyckelord: Bayesian nonlinear filtering, Kalman filter, MAP estimation, optimization

Denna post skapades 2015-05-21. Senast ändrad 2015-12-10.
CPL Pubid: 217369


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Institutioner (Chalmers)

Institutionen för signaler och system, Signalbehandling



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