نتایج جستجو برای: kalman smoother

تعداد نتایج: 19179  

2002
David Barber Peter Sollich

We consider approximate inference in a class of switching linear Gaussian State Space models which includes the switching Kalman Filter and the more general case of switch transitions dependent on the continuous hidden state. The method is a novel form of Gaussian sum smoother consisting of a single forward and backward pass, and compares favourably against a range of competing techniques, incl...

2002
Haris Baltzakis Panos E. Trahanias

In this paper we deal with one of the fundamental problems for mobile robots, namely their ability to produce accurate representations of their environment. We propose an office mapping algorithm that iteratively alternates between a Kalman smoother based localization step and a map features recalculation step. Moreover, a hybrid algorithm with global localization capabilities is employed in th...

2005
David Barber

We consider approximate inference in a class of switching linear Gaussian State Space models which includes the switching Kalman Filter and the more general case of switch transitions dependent on the continuous hidden state. The method is a novel form of Gaussian sum smoother consisting of a single forward and backward pass, and compares favourably against a range of competing techniques, incl...

Journal: :Mathematical biosciences 2011
Abbas Babajani-Feremi Hamid Soltanian-Zadeh

In Part I and Part II of these two companion papers (henceforth called Part I and Part II), we develop and evaluate a variational Bayesian expectation maximization (VBEM) method for model inversion of our multi-area extended neural mass model (MEN). In this paper, we develop the VBEM method to estimate posterior distributions of parameters of MEN. We choose suitable prior distributions for the ...

Journal: :SIAM J. Control and Optimization 2014
Aleksandr Y. Aravkin James V. Burke Gianluigi Pillonetto

Two nonlinear Kalman smoothers are proposed using the Student’s t distribution. The first, which we call the T-Robust smoother, finds the maximum a posteriori (MAP) solution for Gaussian process noise and Student’s t observation noise. It is extremely robust against outliers, outperforming the recently proposed `1-Laplace smoother in extreme situations with data containing 20% or more outliers....

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