abstract: A signal with time-varying frequency content can often be expressed more clearly using a time-frequency representation (TFR), which maps the signal into a two-dimensional function of time and frequency, similar to musical notation. The thesis reviews one of the most commonly used TFRs, the Wigner distribution (WD), and discusses its application in Fourier optics: it is shown that the WD is analogous to the spectral dispersion that results from a diffraction grating, and time and frequency are similarly analogous to a one dimensional spatial coordinate and wavenumber. The grating is compared with a simple polychromator, which is a bank of optical filters. Another well-known TFR is the short time Fourier transform (STFT). Its discrete version can be shown to be equivalent to a filter bank, an array of bandpass filters that enable localized processing of the analysis signals in different sub-bands. This work proposes a signal-adaptive method of generating TFRs. In order to minimize distortion in analyzing a signal, the method modifies the filter bank to consist of non-overlapping rectangular bandpass filters generated using the Butterworth filter design process. The information contained in the resulting TFR can be used to reconstruct the signal, and perfect reconstruction techniques involving quadrature mirror filter banks are compared with a simple Fourier synthesis sum. The optimal filter parameters of the rectangular filters are selected adaptively by minimizing the mean-squared error (MSE) from a pseudo-reconstructed version of the analysis signal. The reconstruction MSE is proposed as an error metric for characterizing TFRs; a practical measure of the error requires normalization and cross correlation with the analysis signal. Simulations were performed to demonstrate the the effectiveness of the new adaptive TFR and its relation to swept-tuned spectrum analyzers. / Dissertation/Thesis / M.S. Electrical Engineering 2012

Classical adaptive signal processors typically utilize assumptions in their derivation. The presence of adequate Gaussian and independent and identically distributed (i.i.d.) input data are central among such assumptions. However, classical processors have a tendency to suffer a degradation in performance when assumptions like these are violated. Worse yet, such degradation is not guaranteed to be proportional to the level of deviation from the assumptions. This dissertation proposes new signal processing algorithms based on aspects of modern robustness theory, including methods to enable adaptivity of presently non-adaptive robust approaches. The contributions presented are the result of research performed jointly in two disciplines, namely robustness theory and adaptive signal processing. This joint consideration of robustness and adaptivity enables improved performance in assumption-violating scenarios—scenarios in which classical adaptive signal processors fail. Three contributions are central to this dissertation. First, a new adaptive diagnostic tool for high-dimension data is developed and shown robust in problematic contamination. Second, a robust data-pre-whitening method is presented based on the new diagnostic tool. Finally, a new suppression-based robust estimator is developed for use with complex-valued adaptive signal processing data. To exercise the proposals and compare their performance to state- of-the art methods, data sets commonly used in statistics as well as Space-Time Adaptive Processing (STAP) radar data, both real and simulated, are processed, and performance is subsequently computed and displayed. The new algorithms are shown to outperform their state-of-the-art counterparts from both a signal-to-interference plus noise ratio (SINR) convergence rate and target detection perspective. / Ph. D.

This research investigates robust demodulation of Gaussian Minimum Shift Keying (GMSK) signals, using demodulator diversity and real-time bit-error-rate (BER) estimation. GMSK is particularly important because of its use in promi- nent wireless standards around the world (GSM, DECT, CDPD, DCS1800, and PCS1900). The dissertation begins with a literature review of GMSK demodu- lation techniques (coherent and noncoherent) and includes an overview of single- channel interference rejection techniques in digital wireless communications. Vari- ous forms of GMSK demodulation are simulated, including the limiter discrimina- tor and di erential demodulator (i.e., twenty-five variations in all). Ten represent new structures and variations. The demodulator performances are evaluated in realistic wireless environments, such as additive white Gaussian noise, co-channel interference, and multipath environments modeled by COST207 and SMRCIM. Certain demodulators are superior to others for particular channel impairments, so that no demodulator is necessarily the best in every channel impairment. This research formally introduces the concept of demodulator diversity, a new idea which consists of a bank of demodulators which simultaneously demodulate the same signal and take advantage of the redundancy in the similar signals. The dissertation also proposes practical real-time BER estimation techniques which have tremendous ramifications for communications. Using Parzen's estimator for probability density functions (pdfs) and Gram-Charlier series approximation for pdfs, BER can be estimated using short observation intervals (10 to 500 training symbols) and, in some cases, without any training sequence. We also introduce new variations of Gram-Charlier estimation using robust estimators. BER (in place of MSE) can now drive adaptive signal processing. Using a cost function and gradient for Parzen's estimator (derived in this paper), BER estimation is applied to demodulator diversity with substantial gains of 1-10 dB in carrier- to-interference ratio over individual receivers in realistic channels (with adaptive selection and weighting). With such gains, a BER-based demodulator diversity scheme can allow the employment of a frequency reuse factor of N = 4, instead of N = 7, with no degradation in performance. A lower reuse factor means more channels are available in a cell, thus increasing overall capacity. The resulting techniques are simple and easily implemented at the mobile. BER estimation techniques can also be used in BER-based equalization and dynamic allocation of resources. / Ph. D.

This thesis demonstrates how a Fast Gradient approximation to a Lattice Filter can be used as an Adaptive Line Enhancer for sampled data consisting of multiple switching tones in correlated noise. A tradeoff analysis is performed with four methods of digital filtering including a conventional Digital Fourier Transform (DFT) algorithm, a Least Mean Squares (LMS) adaptive algorithm, a Fast Recursive Least Squares (Fast RLS) adaptive algorithm, and the Fast Gradient adaptive algorithm. The DFT algorithm is incapable of removing correlations from the incoming noise, and the LMS and Fast RLS algorithms become unstable when a dynamic switching environment is being filtered. The Fast Gradient adaptive algorithm simulated on a computer is robust and capable of converging to an optimal set of FIR filter weights with minimum Mean Squared Error. Parameters for the Fast Gradient algorithm are optimized to provide good filter performance with a minimum number of computations. / Master of Science

Call number: LD2668 .T4 1985 B72 / Master of Science

International Telemetering Conference Proceedings / October 27-30, 1997 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In this paper, the basic ideas of advanced Spread Spectrum Unified Tracking Telemetry & Command System are introduced, the approaches and strategies to reject narrowband interference, multiple access interference and multipath interference are discussed. With effective interference-rejection, the safety and robustness of SS-UTTCS will be improved enormously.

Standard GPS receivers are unable to provide the rate or precision required when used on a small vessel such as an Unmanned Surface Vehicles (USVs). To overcome this, the thesis presents a low cost high rate motion measurement system for an USV with underwater and oceanographic purposes. The work integrates an Inertial Measurement Unit (IMU), a GPS receiver, a flux-gate compass, a tilt sensor and develops a software package, using real time data fusion methods, for an USV to aid in the navigation and control as well as controlling an onboard Acoustic Doppler Current Profiler (ADCP).While ADCPs non-intrusively measure water flow, they suffer from the inability to discriminate between motions in the water column and self-motion. Thus, the vessel motion contamination needs to be removed to analyze the data and the system developed in this thesis provides the motion measurements and processing to accomplish this task. / by Chrystel Gelin. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

In wireless orthogonal frequency division multiple-access (OFDMA) standards, subcarriers are grouped into chunks and a chunk of subcarriers is made as the minimum allocation unit for subcarrier allocation. We investigate the chunk-based resource allocation for OFDMA downlink, where data streams contain packets with diverse bit-errorrate (BER) requirements. Supposing that adaptive transmissions are based on a number of discrete modulation and coding modes, we derive the optimal resource allocation scheme that maximizes the weighted sum of average user rates under the multiple BER and total power constraints. With proper formulation, the relevant optimization problem is cast as an integer linear program (ILP). We can rigorously prove that the zero duality gap holds for the formulated ILP and its dual problem. Furthermore, it is shown that the optimal strategy for this problem can be obtained through Lagrange dual-based gradient iterations with fast convergence and low computational complexity per iteration. Relying on the stochastic optimization tools, we further develop a novel on-line algorithm capable of dynamically learning the underlying channel distribution and asymptotically approaching the optimal strategy without knowledge of intended wireless channels a priori. In addition, we extend the proposed approach to maximizing the a-fair utility functions of average user rates, and show that such a utility maximization can nicely balance the trade-off between the total throughput and fairness among users. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

This study is performed in tandem with numerous experiments performed by the U.S. Navy to characterize the ocean environment in the South Florida region. The research performed in this study includes signal processing steps for isolating ocean phenomena, such as internal waves, in the magnetic field. Raw magnetometer signals, one on shore and one underwater, are processed and removed of common distortions. They are then run through a series of filtering techniques, including frequency domain cancellation (FDC). The results of the filtered magnetic residual are compared to similarly processed Acoustic Doppler Current Profiler (ADCP) data to correlate whether a magnetic signature is caused by ocean phenomena. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Flow over a rough surface is known to radiate sound as a dipole source that is directional. In order to better understand this source, measurements are being made in a wind tunnel using a microphone array. The measurements collected by a microphone array are beamformed to give a source image and can be deconvolved with an assumed point spread function in order to obtain the source levels. This thesis considers alternative analysis algorithms that can be used to analyze wind tunnel data. Only numerical examples of how these algorithms work will be presented and the analysis of real data will be considered in later studies. It will be shown how estimates can be made of the source directivity by comparing the measured data with a theoretical source model and minimizing the error between the model and the measurements. / by Gerard P. Kaufman. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.