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dc.contributorPfister, Henry
dc.creatorBoddikurapati, Sirish
dc.description.abstractEstimating the state of a system from noisy measurements is a problem which arises in a variety of scientific and industrial areas which include signal processing, communications, statistics and econometrics. Recursive filtering is one way to achieve this by incorporating noisy observations as they become available with prior knowledge of the system model. Bayesian methods provide a general framework for dynamic state estimation problems. The central idea behind this recursive Bayesian estimation is computing the probability density function of the state vector of the system conditioned on the measurements. However, the optimal solution to this problem is often intractable because it requires high-dimensional integration. Although we can use the Kalman lter in the case of a linear state space model with Gaussian noise, this method is not optimum for a non-linear and non-Gaussian system model. There are many new methods of filtering for the general case. The main emphasis of this thesis is on one such recently developed filter, the particle lter [2,3,6]. In this thesis, a detailed introduction to particle filters is provided as well as some guidelines for the efficient implementation of the particle lter. The application of particle lters to various communication channels like detection of symbols over the channels, capacity calculation of the channel are discussed.
dc.subjectParticle filtering
dc.subjectSequential Monte Carlo filtering
dc.subjectMarkovian chains
dc.subjectRecursive Bayesian filtering
dc.subjectContinuous-Discrete particle filter
dc.subjectoptical fiber propagation
dc.subjectcapacity of optical fiber
dc.subjectinformation rate using particle filtering
dc.titleSequential Monte Carlo Methods With Applications To Communication Channels

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