Gaussian MAP Filtering Using Kalman Optimization
Artikel i vetenskaplig tidskrift, 2015

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.

Bayesian nonlinear filtering

MAP estimation

Kalman filter



Angel Garcia

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Lennart Svensson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

IEEE Transactions on Automatic Control

0018-9286 (ISSN) 1558-2523 (eISSN)

Vol. 60 5 1336-1349 6963359






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