Engineering Commons^™

Narrow-Band Interference Rejection In Spread Spectrum Using An Eigen Analysis Based Approach, Aparna Vadhri

Theses

A new adaptive technique is suggested for rejecting narrow-band interferences in spread spectrum communications. When data is coded using a pseudo-noise code, the received signal consists of a wide-band signal with almost white spectral properties, thermal noise, and correlated narrow-band interferences. A new approach is proposed which exploits the statistical properties of the received signal via eigenanalysis of the received data. While the energy of the wide-band signal is distributed over all the eigenvalues of the signal autocorrelation matrix, the energy of the interference is concentrated in a few large eigenvalues. Hence, the eigenvectors corresponding to the large eigenvalues are …

Simplification Of The Generalized Adaptive Neural Filter And Comparative Studies With Other Nonlinear Filters, Henry Steven Hanek

Theses

Recently, a new class of adaptive filters called Generalized Adaptive Neural Filters (GANFs) has emerged. They share many characteristics in common with stack filters, include all stack filters as a subset. The GANFs allow a very efficient hardware implementation once they are trained. However, there are some problems associated with GANFs. Three of these arc slow training speeds and the difficulty in choosing a filter structure and neural operator.

This thesis begins with a tutorial on filtering and traces the GANF development up through its origin -- the stack filter. After the GANF is covered in reasonable depth, its use …

Adaptive Stack Filtering By Lms And Perceptron Learning, Yu-Chou Huang

Theses

Stack filters are a class of sliding?window nonlinear digital filters that possess the weak superposition property(threshold decomposition) and the ordering property known as the stacking property. They have been demonstrated to be robust in suppressing noise. Two methods are introduced in this thesis to adaptively configure a stack filter. One is by employing the Least Mean Square(LMS) algorithm and the other is based on Perceptron learning.

Experimental results are presented to demonstrate the effectiveness of our methods to noise suppression.