Estimating 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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-12-7537 |
| Date | 2009 December 1900 |
| Creators | Boddikurapati, Sirish |
| Contributors | Pfister, Henry |
| Source Sets | Texas A and M University |
| Language | English |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | application/pdf |
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