Advances in Spectrum Analysis and Array Processing, Volume III

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ISBN 10: 0130615404 ISBN 13: 9780130615404

Publisher: Prentice Hall, 1995

In this, the third and final volume in the series, ten experts investigate a broad range of topics covering fundamental issues and applications in popular and new algorithms for Spectral Analysis and Array Processing. It covers optimal model-based processing techniques for the detection of multiple narrowband sources; two-dimensional angle estimation; direction-finding algorithms for closely-spaced source scenarios; and the use of neural networks in solving source location problems.

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From the Back Cover

This is the third and final volume in this series. In this volume, ten experts investigate a broad range of topics coverig fundamental issues and applications in popular and new algorithms for Spectral Analysis and Array Processing. Includes: Optimal model-based processing techniques for the detection of multiple narrowband sources; two- dimensional angle estimation; direction-finding algorithms for closely-spaced source scenarios; and the use of neural networks in solving source location problems.For engineers and scientists interested in spectral analysis.

From the Inside Flap

This is the third and the final volume in a series of books which have had the honor of editing on Advances in Spectrum Analysis and Array Processing.

The first two volumes were published in 1991. The present volume is organized in 10 chapters.

Chapter 1 by Mati Wax presents optimal and suboptimal model-based processing techniques for the detection,localization, and beamforming of multiple narrowband sources by means of passive arrays.

Chapter 2 by Jack Jachner and Harry Lee utilizes the Cramer Rao lower bound on parameter estimation variance to identify fundamental limitations of unbiased direction-finding algorithms for closely spaced source scenarios.

Chapter 3 by W. Radich, R. Hamza, and Kevin Buckley addresses another fundamental issue, namely, the robustness of subspace-based direction-finding algorithms.

Chapter 4 by Lynn Kirlin, Emily Su, and Brad Hedstrom uses analogy with phase-back loop solutions to shed further light on the array processing problem.

Chapter 5 by Cherian Mathews and Michael Zoltowski focuses on the special case of uniform circular arrays, describing subspace-based methods for two-dimensional angle estimation. Chapter 6 by Max Wong, Qiang Wu, and Peter Stoica describes the development of a new technique called generalized correlation decomposition and its application to the array processing problem in an unknown noise background.

Chapter 7 by Jacob Sheinvald and Mati Wax presents a new technique for solving the array processing problem, which permits sampling arbitrary subarrays sequentially in a computationally efficient manner.

Chapter 8 by Alfred Hero III and Ronald DeLap develops task-specific criteria for adaptive beamforming, with the aim of optimizing the best achievable signal detection or parameter estimation at the output of the beamformer.

Chapter 9 by Mithat Dogan and Jerry Mendel exploits cumulants as a tool for extracting more phase information than is possible by using only second-order statistics as is ordinarily the case. By so doing, significant improvements in the performance of array processing systems are realized. Finally,

Chapter 10 by Henry Leung and Titus Lo describes the use of another new tool, neural networks, as the basis for using array data to solve the inverse problem of source location.

Much of the material presented in this book has not appeared in book form before. It has been my distinct pleasure to have worked with these fine researchers in editing the book.

Simon Haykin