Original paper

Particle Filtering With Dependent Noise Processes

Volume: 60, Issue: 9, Pages: 4497 - 4508
Published: Sep 1, 2012
Abstract
Modeling physical systems often leads to discrete time state-space models with dependent process and measurement noises. For linear Gaussian models, the Kalman filter handles this case, as is well described in literature. However, for nonlinear or non-Gaussian models, the particle filter as described in literature provides a general solution only for the case of independent noise. Here, we present an extended theory of the particle filter for...
Paper Details
Title
Particle Filtering With Dependent Noise Processes
Published Date
Sep 1, 2012
Volume
60
Issue
9
Pages
4497 - 4508
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