Array Signal Processing

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.

Contenu

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.