Sequential Monte Carlo Methods With Applications To Communication Channels
MetadataShow full item record
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.
Sequential Monte Carlo filtering
Recursive Bayesian filtering
Continuous-Discrete particle filter
optical fiber propagation
capacity of optical fiber
information rate using particle filtering
Boddikurapati, Sirish (2009). Sequential Monte Carlo Methods With Applications To Communication Channels. Master's thesis, Texas A&M University. Available electronically from
Showing items related by title, author, creator and subject.
Deep Off-Shore Combustion Air Filtering System Analysis - Off-Shore Filter Systems Analysis to Peak GTG Availability and Reliability in Harsh Condition Markovits, Fernando; Carlos, Francisco; Benson, Jim; Kohn, Joshua (Texas A&M University. Turbomachinery Laboratories, 2013)
Sekar, Vikram (2012-10-19)The main purpose of this dissertation was to address key issues in the design and analysis of RF/microwave filters for wireless applications. Since RF/microwave filters are one of the bulkiest parts of communication ...
Multiple Hypothesis Tracking Filter with Lambert Post Filtering for Multiple Space Object Tracking Mishra, Utkarsh Ranjan (2019-04-25)The data association problem occurs when there is uncertainty and ambiguity in assessing the origin of measurements especially when the measurements are being made from multiple locations at multiple times. For example, ...