Title :
Bootstrap Particle Filtering
Author_Institution :
Univ. of California, Santa Barbara
fDate :
7/1/2007 12:00:00 AM
Abstract :
This article provides an overview of nonlinear statistical signal processing based on the Bayesian paradigm. The next-generation processors are well founded on MC simulation-based sampling techniques. The development of the sequential Bayesian processor is reviewed using the state-space models. The popular bootstrap algorithm was outlined and applied to an ocean acoustic synthetic aperture towed array target tracking problem to test the performance of a particle filtering technique.
Keywords :
Bayes methods; Monte Carlo methods; acoustic signal processing; particle filtering (numerical methods); signal sampling; state-space methods; target tracking; underwater sound; Bayesian processor; MC-simulation based sampling technique; bootstrap algorithm; bootstrap particle filtering; nonlinear statistical signal processing; ocean acoustic synthetic aperture; state-space model; target tracking; Acoustic arrays; Acoustic signal processing; Acoustic testing; Array signal processing; Bayesian methods; Filtering algorithms; Oceans; Signal processing algorithms; Signal sampling; Target tracking;
Journal_Title :
Signal Processing Magazine, IEEE
DOI :
10.1109/MSP.2007.4286566
