Abstract

We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide application in adaptive signal processing. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling and Markov chain iterations. Examples from target tracking and digital communication applications are provided to demonstrate the effectiveness of these novel statistical signal processing techniques.

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