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This paper introduces the key principles and applications of particle filtering. Particle filters are a class of modern sequential Monte Carlo Bayesian methods based on point mass representation of posterior probability density. They are highly useful in parameter estimation when dealing with nonlinear system models and non-Gaussian noise. After summarizing the basic algorithms used in particle filters,...
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