Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/11973
Authors: Isanaka, Srikanth
Issue Date: 2009
Abstract: Estimating the state of the system from noisy measurements is being increasingly used in many application areas. which include, signal processing, communications, statistics and econometrics. Filtering is a way to achieve this by incorporating noisy observations as they become available with prior knowledge of the system model. Due to the dramatic increase in the number of users and their demand for more advanced services, the need for fast and accurate filtering techniques in digital communications, capable of coping with challenging transmission conditions, is becoming more and more prevalent. Bayesian methods form a rigorous general frame work for dynamic state estimation problems. The central idea to this recursive Bayesian estimation is to determine the probability density function of the state vector of the systems conditioned on the available measurements. However, the optimal exact solution to this Bayesian filtering problem is intractable since it requires high dimensional integration. Kalman filter provides an optimal solution in case of linear systems and Gaussian noise. For practical nonlinear filtering applications, extended Kalman filter, which is based on an assumption of Gaussian noise, yields approximate solutions. Particle filtering algorithms, which are developed independently in various engineering fields, provides a numerical solution to the non-tractable recursive Bayesian estimation problem in case of non-linear and non-Gaussian systems. In this dissertation work, we have used the state space model approach for deriving the particle filtering algorithm for blind detection in various systems namely SISO, MIMO, OFDM, with the use of Kalman filtering algorithm. Particle filters are sequential Monte Carlo methods that use a point mass representation of probability densities in order to propagate the required statistical properties for state estimation. MIMO-OFDM systems can achieve higher data rates over broadband wireless channels. The blind detection in differentially encoded MIMO-OFDM systems using particle filtering algorithm is also been exploited. For simulation MATLAB is used and it is demonstrated through simulation results that the performance of particle filtering approach to blind detection is close to the optimal MLSE receiver.
Other Identifiers: M.Tech
Research Supervisor/ Guide: Mehra, D. K.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' THESES (E & C)

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