Topics in Non-Gaussian Signal Processing

1. Front Matter

   Pages i-xii

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2. Modeling and Characterization

   1. Front Matter

      Pages 1-1

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   2. Bispectral Characterization of Ocean Acoustic Time Series: Nonlinearity and Non-Gaussianity

      • Patrick L. Brockett, Melvin Hinich, Gary R. Wilson

      Pages 2-16

   3. Class a Modeling of Ocean Acoustic Noise Processes

      • Dennis R. Powell, Gary R. Wilson

      Pages 17-28

   4. Statistical Characteristics of Ocean Acoustic Noise Processes

      • Fredrick W. Machell, Clark S. Penrod, Glen E. Ellis

      Pages 29-57

   5. Conditionally Linear and Non-Gaussian Processes

      • R. R. Mohler, W. J. Kolodziej

      Pages 58-72

   6. A Graphical Tool for Distribution and Correlation Analysis of Multiple Time Series

      • Edward J. Wegman, Chris Shull

      Pages 73-86

3. Filtering, Estimation and Regression

   1. Front Matter

      Pages 87-87

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   2. Comments on Structure and Estimation for NonGaussian Linear Processes

      • M. Rosenblatt

      Pages 88-97

   3. Harmonizable Signal Extraction, Filtering and Sampling

      • M. M. Rao

      Pages 98-117

   4. Fisher Consistency of Am-Estimates of the Autoregression Parameter Using Hard Rejection Filter Cleaners

      • R. D. Martin, V. J. Yohai

      Pages 118-127

   5. Bayes Least Squares Linear Regression Is Asymptotically Full Bayes: Estimation of Spectral Densities

      • H. D. Brunk

      Pages 128-147

4. Detection and Signal Extraction

   1. Front Matter

      Pages 148-148

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   2. Signal Detection for Spherically Exchangeable (Se) Stochastic Processes

      • C. B. Bell

      Pages 149-167

   3. Contributions to Non-Gaussian Signal Processing

      • Roger F. Dwyer

      Pages 168-183

   4. Detection of Signals in the Presence of Strong, Signal-Like Interference and Impulse Noise

      • D. W. Tufts, I. P. Kirsteins, P. F. Swaszek, A. J. Efron, C. D. Melissinos

      Pages 184-196

   5. On NonGaussian Signal Detection and Channel Capacity

      • Charles R. Baker

      Pages 197-208

   6. Detection in a Non-Gaussian Environment: Weak and Fading Narrowband Signals

      • Stuart C. Schwartz, John B. Thomas

      Pages 209-227

   7. Energy Detection in the Ocean Acoustic Environment

      • Fredrick W. Machell, Clark S. Penrod

      Pages 228-235

Non-Gaussian Signal Processing is a child of a technological push. It is evident that we are moving from an era of simple signal processing with relatively primitive electronic cir­ cuits to one in which digital processing systems, in a combined hardware-software configura.­ tion, are quite capable of implementing advanced mathematical and statistical procedures. Moreover, as these processing techniques become more sophisticated and powerful, the sharper resolution of the resulting system brings into question the classic distributional assumptions of Gaussianity for both noise and signal processes. This in turn opens the door to a fundamental reexamination of structure and inference methods for non-Gaussian sto­ chastic processes together with the application of such processes as models in the context of filtering, estimation, detection and signal extraction. Based on the premise that such a fun­ damental reexamination was timely, in 1981 the Office of Naval Research initiated a research effort in Non-Gaussian Signal Processing under the Selected Research Opportunities Program.

• Signal
• detection
• filter
• filtering
• filters
• modeling
• signal processing

• Center for Computational Statistics and Probability, George Mason University, Fairfax, USA

  Edward J. Wegman

• Department of Electrical Engineering, Princeton University, Princeton, USA

  Stuart C. Schwartz, John B. Thomas

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