Nonlinear Statistical Signal Processing: A Particle Filtering Approach
Description:
A introduction to particle filtering is discussed starting with an overview of Bayesian inference from batch to sequential processors. Once the evolving Bayesian paradigm is established, simulation-based methods using sampling theory and Monte Carlo realizations are discussed. Here the usual limitations of nonlinear approximations and non-gaussian processes prevalent in classical nonlinear processing algorithms (e.g. Kalman filters) are no longer a restriction to perform Bayesian inference. It …
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Date:
September 19, 2007
Creator:
Candy, J
Partner:
UNT Libraries Government Documents Department