published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper abstractly introduces the configuration, main modules, and software of the ground detection system for re-entry vehicle' s telemetry. It focuses on introducing intelligent high bit rate CAMAC(Computer Automic Mete And Control) modules, high frequency CAMAC modules, adaption between CAMAC bus and telemetry bus, and writing high bit rate data into disk under the control of CCU (Central Control Unit), etc.

Many military communications systems utilize frequency-hopped spread spectrum waveforms to protect against jamming and enemy detection. These waveforms may be subjected not only to intentional jamming but may also be unintentionally jammed by other communications signals. While some systems can overcome inband interference with more signal power, covert systems may be limited to small amounts of transmitted power. The objective of this thesis was to investigate a method for resolving a frequency-hopped signal embedded in interference waveforms. With exponential averaging in the frequency domain, the spectra of the interfering signals can be estimated as long as they are present over a period longer than that of the frequency-hopped signal. Certain FFT sizes and weights are more beneficial to achieving this estimate than others. The interference estimate can be used to extract the desired frequency-hopped signal through spectral division of the received signal with the estimate. This technique is designated as noise-normalization. Simulations in MATLAB demonstrate the use of the technique and show how the desired signal can be resolved.

QC 351 A7 no. 65 / The significance of signal-dependent noise is discussed. Particular emphasis is placed on the type of multiplicative noise present in the density variations in a photographic emulsion. A theoretical treatment of the effect of multiplicative noise on signal detection and signal discrimination problems is presented. Optimum test statistics are derived for processing a sampled message to detect the presence of a known signal. Multiplicative noise described by Poisson and Gaussian statistics is considered. The expressions are extended to include the two-signal discrimination problem. Two-dimensional signal fields in the presence of multiplicative noise are simulated in a computer and processed for optimum signal detection according to the two derived methods. These results are compared to the results of processing based on the assumption of stationary noise statistics. This comparison reveals that modest improvements (20% to 30% reduction in false alarm rate) are obtained when the signal-dependent nature of the noise statistics is considered. The effects of signal-to-noise ratio, signal structure, and changing background level are also investigated. An example of optimum signal discrimination using circles and squares as signals in multiplicative noise is reported. An improvement in the percentage of correctly identified signals is again observed when the proper test statistic is used. Two examples of signal filtering in the presence of signal-dependent noise are included. The first concerns the processing of a real star field to determine the location of weak stars. The second is an illustration of the signal information contained in the noise spectrum of a message recorded on a common photographic film.

Combination of p-values from multiple independent tests has been widely studied since 1930's. To find the optimal combination methods, various combiners such as Fisher's method, inverse normal transformation, maximal p-value, minimal p-value, etc. have been compared by different criteria. In this work, we focus on the criterion of Bahadur efficiency, and compare various methods under the TFisher. As a recently developed general family of combiners, TFisher cover Fisher's method, the rank truncated product method (RTP), the truncation product method (TPM, or the hard-thresholding method), soft-thresholding method, minimal p-value method, etc. Through the Bahadur asymptotics, we better understand the relative performance of these methods. In particular, through calculating the Bahadur exact slopes for the problem of detecting sparse signals, we reveal the relative advantages of truncation versus non-truncation, hard-thresholding versus soft-thresholding. As a result, the soft thresholding method is shown superior when signal strength is relatively weak and the ratio between the sample size of each p-value and the number of combining p-values is small.

Typescript (photocopy). / Digitized by Kansas Correctional Industries / Department: Electrical and Computer Engineering.

This thesis addresses some problems that arise in signal processing when the noise is impulsive and follows a heavy tailed distribution. After reviewing several of the more well known heavy- tailed distributions the common problem of which of these hest models the observations is considered. To this end, a test is proposed for the symmetric alpha stable distribution. The test threshold is found using both asymptotic theory and parametric bootstrap resampling. In doing so, some modifications are proposed for Koutrouvelis' estimator of the symmetric alpha stable distributions parameters that improve performance. In electrical systems impulsive noise is generated externally to the receiver while thermal Gaussian noise is generated internally by the receiver electronics, the resultant noise is an additive combination of these two independent sources. A characteristic function domain estimator for the parameters of the resultant distribution is developed for the case when the impulsive noise is modeled by a symmetric alpha stable distribution. Having concentrated on validation and parameter estimation for the noise model, some problems in signal detection and estimation are considered. Detection of the number of sources impinging on an array is an important first. step in many array processing problems for which the development of optimal methods can be complicated even in the Gaussian case. Here, a multiple hypothesis test for the equality of the eigenvalues of the sample array covariance is proposed. / The nonparametric bootstrap is used to estimate the distributions of the test statistics removing the assumption of Gaussianity and offering improved performance for heavy tailed observations. Finally, some robust estimators are proposed for estimating parametric signals in additive noise. These are based on M-estimators but implicitly incorporate an estimate of the noise distribution. enabling the estimator to adapt to the unknown noise distribution. Two estimators are developed, one uses a nonparametric kernel density estimator while the other models the score function of the noise distribution with a linear combination of basis functions.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (M.S.)--State University of New York at Binghamton, Department of Electrical and Computer Engineering, 2006. / Includes bibliographical references.