Preface
1 Introduction
1.1 Signal Detection and Enhancement
1.2 Signal Characterization
1.3 Tracking
1.4 Filtering vs. Parameter Estimation
Problems
2 Signals in Space and Time
2.1 Coordinate Systems
2.2 Propagating Waves
2.3 Dispersion and Attenuation
2.4 Refraction and Diffraction
2.5 Wavenumber-Frequency Space
2.6 Random Fields
2.7 Signal and Noise Assumptions
Summary
Problems
3 Apertures and Arrays
3.1 Finite Continuous Apertures
3.2 Spatial Sampling
3.3 Arrays of Discrete Sensors
Summary
Problems
4 Beamforming
4.1 Delay-and-Sum Beamforming
4.2 Space-Time Filtering
4.3 Filter-and-Sum Beamforming
4.4 Frequency-Domain Beamforming
4.5 Array Gain
4.6 Resolution
4.7 Sampling in Time
4.8 Discrete-Time Beamforming
4.9 Averaging in Time and Space
Summary
Problems
5 Detection Theory
5.1 Elementary Hypothesis Testing
5.2 Hypothesis Testing in Presence of Unknowns
5.3 Detection of Signals in Gaussian Noise
5.4 Detection in Presence of Uncertainties
5.5 Detection-Based Array Processing Algorithms
Summary
Problems
6 Estimation Theory
6.1 Terminology in Estimation Theory
6.2 Parameter Estimation
6.3 Signal Parameter Estimation
6.4 Linear Signal Waveform Estimation
6.5 Spectral Estimation
Summary
Problems
7 Adaptive Array Processing
7.1 Signal Parameter Estimation
7.2 Constrained Optimization Methods
7.3 Eigenanalysis Methods
7.4 Robust Adaptive Array Processing
7.5 Dynamic Adaptive Methods
Summary
Problems
8 Tracking
8.1 Source Motion Models
8.2 Single-Array Location Estimate Properties
8.3 Prediction-Correction Algorithms
8.4 Tracking Based on Kalman Filtering
8.5 Multiarray Tracking in Clutter
Summary
Problems
A Probability and Stochastic Processes
A.1 Foundations of Probability Theory
A.2 Stochastic Processes
B Matrix Theory
B.1 Basic Definitions
B.2 Basic Matrix Forms
B.3 Operations on Matrices
B.4 Quadratic Forms
B.5 Matrix Eigenanalysis
B.6 Projection Matrices
C Optimization Theory
C.1 Unconstrained Optimization
C.2 Constrained Optimization
Bibliography
List of Symbols
Index