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Presenter: Md. Zulfiquar Ali Bhotto
Supervisor: Dr. Andreas Antoniou

Date: Fri, December 16, 2011
Time: 09:00:00 - 00:00:00
Place: ECS 660

ABSTRACT

ABSTRACT

New adaptive-filtering algorithms, also known as adaptation algorithms, are proposed. The new algorithms can be broadly classified into two categories, namely, steepest-descent and Newton-type adaptation algorithms. Several new methods have been used to bring about improvements regarding the speed of convergence, steady-state misalignment, robustness with respect to impulsive noise, re-adaptation capability, and computational load of the proposed algorithms.

In chapters 2, 3, and 8, several adaptation algorithms are developed that belong to the steepest descent family. The algorithms of chapters 2 and 3 use two error bounds with the aim of reducing the computational load, achieving robust performance with respect to impulsive noise, good tracking capability and significantly reduced steady-state misalignment. The error bounds can be either perspecified or estimated using an update formula that incorporates a modified variance estimator. Analyses pertaining to the steady-state mean-square error (MSE) of some of these algorithms are also presented. The algorithms in chapter 8 use a so-called iterative/shrinkage method to obtain a variable step size by which improved convergence characteristics can be achieved compared to those in other state-of-the-art competing algorithms.

Several adaptation algorithms that belong to the Newton family are developed in chapters 4-6 with the aim of achieving robust performance with respect to impulsive noise, reduced steady-state misalignment, and good tracking capability without compromising the initial speed of convergence. The algorithm in chapter 4 imposes a bound on the L₁ norm of the gain vector in the crosscorrealation update formula to achieve robust performance with respect to impulsive noise in stationary environments. In addition to that a variable forgetting factor is also used to achieve good tracking performance for applications in nonstationary environments. The algorithm in chapter 5 is developed to achieve a reduced steady-state misalignment and improved convergence speed and a reduced computational load. The algorithm in chapter 6 is essentially an extension of the algorithm in chapter 5 designed to achieve robust performance with respect to impulsive noise and a reduced computational load. Analyses concerning the asymptotic stability and steady-state MSE of these algorithms are also presented.

An algorithm that minimizes Reny's entropy of the error signal is developed in chapter 7 with the aim of achieving faster convergence and reduced steady-state misalignment compared to those in other algorithms of this family.

Simulation results are presented that demonstrate the superior convergence characteristics of the proposed algorithms with respect to state-of-the-art competing algorithms of the same family in network-echo cancellation, acoustic-echo cancellation, system-identification, interference-cancellation, time-series perdiction, and time-series filtering applications. In addition, simulation results concerning system-identification applications are also used to verify the accuracy of the MSE analyses presented.