1 Introduction.- 1.1 Introduction.- 1.2 Organization of the Book.- 1.3 Notations and Preliminaries.- 2 Detection of Multiple Signals.- 2.1 Signals and Noise.- 2.2 Conventional Techniques.- 2.2.1 Beamformer.- 2.2.2 Capon's Minimum Variance Estimator.- 2.2.3 Linear Prediction Method.- 2.3 Eigenvector-Based Techniques.- 2.3.1 Completely Coherent Case.- 2.3.2 Symmetric Array Scheme: Coherent Sources in a Correlated Scene.- 2.3.3 Spatial Smoothing Schemes: Direction Finding in a Coherent Environment.- 2.4 Augmentation and Other Techniques.- 2.4.1 Augmentation Technique.- 2.4.2 ESPRIT, TLS-ESPRIT and GEESE.- 2.4.3 Direction Finding Using First Order Statistics.- Appendix 2.A Coherent and Correlated Signal Scene.- Appendix 2.B Program Listings.- Problems.- References.- 3 Performance Analysis.- 3.1 Introduction.- 3.2 The Maximum Likelihood Estimate of the Covariance Matrix and Some Related Distributions.- 3.3 Performance Analysis of Covariance Based Eigenvector Techniques: MUSIC and Spatial Smoothing Schemes.- 3.3.1 Asymptotic Distribution of Eigenparameters Associated with Smoothed Sample Covariance Matrices.- 3.3.2 Two-Source Case - Uncorrelated and Coherent Scene.- 3.4 Performance Evaluation of GEESE Scheme.- 3.4.1 The Least Favorable Configuration (J = K).- 3.4.2 The Most Favorable Configuration (J = M - 1).- 3.5 Estimation of Number of Signals.- Appendix 3.A The Complex Wishart Distribution.- Appendix 3.B Equivalence of Eigenvectors.- Appendix 3.C Eigenparameters in a Two Source Case.- Problems.- References.- 4 Estimation of Multiple Signals.- 4.1 Introduction.- 4.2 Optimum Processing: Steady State Performance and the Wiener Solution.- 4.3 Implementation of the Wiener Solution.- 4.3.1 The Method of Steepest Descent.- 4.3.2 The Least Mean Square (LMS) Algorithm.- 4.3.3 Direct Implementation by Inversion of the Sample Covariance Matrix.- Problems.- References.
This book is intended as an introduction to array signal process ing, where the principal objectives are to make use of the available multiple sensor information in an efficient manner to detect and possi bly estimate the signals and their parameters present in the scene. The advantages of using an array in place of a single receiver have extended its applicability into many fields including radar, sonar, com munications, astronomy, seismology and ultrasonics. The primary emphasis here is to focus on the detection problem and the estimation problem from a signal processing viewpoint. Most of the contents are derived from readily available sources in the literature, although a cer tain amount of original material has been included. This book can be used both as a graduate textbook and as a reference book for engineers and researchers. The material presented here can be readily understood by readers having a back ground in basic probability theory and stochastic processes. A prelim inary course in detection and estimation theory, though not essential, may make the reading easy. In fact this book can be used in a one semester course following probability theory and stochastic processes.
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