Engineering Commons^™

Hyperspectral-Based Adaptive Matched Filter Detector Error As A Function Of Atmospheric Profile Estimation, Allan W. Yarbrough

Theses and Dissertations

Hyperspectral imagery is collected as radiance data. This data is a function of multiple variables: the radiation profile of the light source, the reflectance of the target, and the absorption and scattering profile of the medium through which the radiation travels as it reflects off the target and reaches the imager. Accurate target detection requires that the collected image matches as closely as possible the known "true" target in the classification database. Therefore, the effect of the radiation source and the atmosphere must be removed before detection is attempted. While the spectrum of solar light is relatively stable, the effect …

Managing Bandwidth And Traffic Via Bundling And Filtration In Large-Scale Distributed Simulations, Roberto C. Sánchez

Theses and Dissertations

Research has shown that bandwidth can be a limiting factor in the performance of distributed simulations. The Air Force's Distributed Mission Operations Center (DMOC) periodically hosts one of the largest distributed simulation events in the world. The engineers at the DMOC have dealt with the difficult problem of limited bandwidth by implementing application level filters that process all DIS PDUs between the various networks connected to the exercise. This thesis examines their implemented filter and proposes: adaptive range-based filtering and bundling together of PDUs. The goals are to reduce the number of PDUs passed by the adaptive filter and to …

Airborne Radar Interference Suppression Using Adaptive Three-Dimensional Techniques, Todd B. Hale

Theses and Dissertations

This research advances adaptive interference suppression techniques for airborne radar, addressing the problem of target detection within severe interference environments characterized by high ground clutter levels, levels, noise jammer infiltration, and strong discrete interferers. Two-dimensional (2D) Space-Time Adaptive Processing (STAP) concepts are extended into three-dimensions (3D) by casting each major 2D STAP research area into a 3D framework. The work first develops an appropriate 3D data model with provisions for range ambiguous clutter returns. Adaptive 3D development begins with two factored approaches, 3D Factored Time-Space (3D-FTS) and Elevation-Joint Domain Localized (Elev-JDL). The 3D adaptive development continues with optimal techniques, i.e., …

Adaptive Notch Filter For Single And Multiple Narrow-Band Interference, Choy Chun Sin

Theses : Honours

In this project, the adaptive notch filter for single and Multiple narrow-band interference is implemented using simplified LMS algorithm. Performances of the LMS adaptive algorithms is evaluated and analysed through simulation on the computer using Matlab. The algorithm are then written in C programme and implemented using Texas Instrument Tool which consist of TMS320C54x EMV board and Code Composer Studio.

Adaptive Notch Filter For Single And Multiple Narrow-Band Interference, Selina Kuek Lin Mei

Theses : Honours

In this project, the adaptive notch filter for single and Multiple narrow-band interference is implemented using simplified LMS algorithm. Performances of the LMS adaptive algorithms is evaluated and analysed through simulation on the computer using Matlab. The algorithm are then written in C programme and implemented using Texas Instrument Tool which consist of TMS320C54x EMV board and Code Composer Studio.

Dual Channel Matched Filtering And Space-Time Adaptive Processing, Scott D. Berger

Theses and Dissertations

We propose a dual channel matched filtering system that addresses two key challenges in the practical implementation of a single channel matched filtering system: secondary data support and computational cost. We demonstrated that the dual channel system requires half the secondary data to achieve nearly the same signal-to-interference plus noise ratio (SINR) as an equivalent single channel system. The key to the dual channel system is the block diagonalization of the interference plus noise correlation matrix with a fixed transformation. We investigated the application of this dual channel concept to the problem of space-time adaptive processing (STAP), referring to the …

Building Lms Adaptive Filters With Register-Based Fpgas, Li Ding

Electrical & Computer Engineering Theses & Dissertations

In this thesis, an 8-bit least mean squares (LMS) adaptive digital filter with 16 coefficients is implemented on a single FPGA, using the MaxPlus+2 design environment. The system is constructed as a hierarchical structure, using four different levels of design hierarchy. Subdesigns at each level of the hierarchy project are complied, fitted and simulated to test and verify their functional correctness. The complete system is tested and verified by checking the MaxPlus+2 simulator results against fixed-point integer arithmetic simulations written in Matlab. The resulting adaptive filter is subsequently mapped to the Alters FLEX I OK20TC144-3 device, resulting in a 14,500 …

Interference Suppression For Spread Spectrum Signals Using Adaptive Beamforming And Adaptive Temporal Filter, Wonjin Park

Theses and Dissertations

Interference and jamming signals are a serious concern in an operational military communication environment. This thesis examines the utility and performance of combining adaptive temporal filtering with adaptive spatial filtering (i.e. adaptive beamforming) to improve the signal-to-jammer ratio (SJR) in the presence of narrowband and wideband interference. Adaptive temporal filters are used for narrowband interference suppression while adaptive beamforming is used to suppress wideband interference signals. A procedure is presented for the design and implementation of a linear constraints minimum variance generalized sidelobe canceler (LCMV-GSC) beamformer. The adaptive beamformer processes the desired signal with unity gain while simultaneously and adaptively …

Variable Step Size Lms Adaptive Filters With Delayed Coefficient Updating, Guixian Xu

Electrical & Computer Engineering Theses & Dissertations

A new approach to delayed LMS adaptive filtering is presented, which uses a variable step size for coefficient updating to increase convergence speed and improve tracking characteristics. The new algorithm, called delayed variable step size LMS (DVLMS), is explained, analyzed, and simulated to experimentally determine performance characteristics. Three different strategies for adjusting the step size are examined, and their performance is compared. Also, simulation results are presented to show that the proposed DVLMS systems provide faster convergence and lower mis-adjustment than previously proposed DLMS systems.

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 …

Adaptive Notch Filter, Yuchen Huang

Dissertations and Theses

The thesis presents a new adaptive notch filter (ANF) algorithm that is more accurate and efficient and has a faster convergent rate than previous ANF algorithms. In 1985, Nehorai designed an infinite impulse response (UR) ANF algorithm that has many advantages over previous ANF algorithms. It requires a minimal number of parameters with constrained poles and zeros. It has higher stability and sharper notches than any ANF algorithm until now. Because of the special filter structure and the recursive prediction error (RPE) method, however, the algorithm is very sensitive to the initial estimate of the filter coefficient and its covariance. …

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 …

A Walsh-Domain Adaptive Filter, Larry J. Duvall

Theses and Dissertations

Two Walsh-domain dyadic convolution adaptive filters are developed using a circular convolution frequency-domain filter (FDF1) and the Fast LMS adaptive filter (FDF2): WDF1 and WDF2 respectively. General theory of time- domain adaptive filters and a theoretical analysis of the FDF1, FDF2, WDF1, and WDF2 filters are presented. WDF1 and WDF2 software implementation are shown to be error free. A time-domain filter (TDF) and a FDF2 frequency-domain filter (FDF) are implemented for comparison testing. The WDF1, WDF2, TDF, and FDF filters are tested using time-shifted sinusoidal and rectangular noisy and noiseless signals. WDF1 and WDF2 are shown to converge faster and …

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.