1 Introduction 1.1 Main Contents of the Book 1.2 Some Real Examples of Array 1.3 Chapter Summary 1.4 Chapter Assignments 2 Array Signal Processing 2.1 Plane Wave and Array 2.2 Uniform Linear Array.Uniform Circular Array and Uniform Plane Array 2.2.1 ULA(uniform linear alray) 2.2.2 Array Response and Pattern of ULA 2.2.3 UCA(uniform circular array) 2.2.4 UPA(uniform plane array) 2.3 Statistical Model of Array Signal Processing 2.3.1 Time Delay of Narrow-band Signal 2.3.2 Continuous-time Channel Model 2.3.3 Statistical Model of Array Signal Processing 2.4 Beamforming 2.4.1 Optimal Weight Vector of Beamforming 2.4.2 Bartlett Beamformer 2.4.3 Capon Beamformer 2.5 MUSIC Algorithm 2.5.1 Basic MUSIC Algorithm 2.5.2 Improvement of MUSIC Algorithm 2.5.3 Root-MUSIC Algorithm 2.6 ESPIUT Algorithm 2.6.1 Basic ESP?T Algorithm 2.6.2 TLS.ESPIuT Algorithm 2.7 Maximum Likefihood Method 2.7.1 Deterministic M[L 2.7.2 Stochastic MrL 2.8 Iterative Quadratic Maximum Likelihood Method 2.8.1 Sub.space Fitting 2.8.2 IQML 2.8.3 MODE Algorithm and Weighted Sub-space Fitting Algorithm 2.9 Chapter Summary 2.10 Chapter Assignments 3 Adaptive Array Signal Processing 3.1 Theorems of Adaptive Antenna System 3.1.1 Impulse Response of Vector Channel and Spafial Characteristics 3.1.2 Optimal Weight Vector of Adaptive Array 3.1.3 Adaptive Algorithm with Weight Vector Updating 3.2 Influences of Multipath to the Optimal Spatial Filtering 3.3 Stochastic Blind Beamforming 3.3.1 Blind Beamforming based on High-order Cumulant 3.3.2 Blind Beamforming Based on Cyclic Statistics 3.4 Deterministic Blind Beamforming 3.4.1 Homogeneous MⅣO Model of the Channel
3.4.2 Deterministic Blind Beamforming 3.5 Blind Signal Separation 3.5.1 Blind Identifiabilit、 3.5.2 Equivariant Signal Separation 3.5.3 Second.order Identification Method 3.5.4 Joint Diagonalization of Multiple Matrices 3.6 Neural Networks MethOd Of Blind Signal Separation 3.6.1 Independent Component Analysis and Principal Component Analysis 3.6.2 Neural Network Structure of Blind Signal Separation 3.6.3 Natural Gradient Algorithm of Blind Signal Separation 3.7 Least Square Constant Modulus Algorithm 3.7.1 Steepest Descent CM Algorithm 3.7.2 Least Square CM Algorithm(LS。CMA) 3.7.3 Sub-Gaussian and Super.Gaussian Signal 3.7.4 CM Cost Function 3.8 Constant Modulus Array 3.8.1 CM Array and Adaptive Signal Canceller 3.8.2 Performance Analysis 3.8.3 CM Array to Recover Multiple Sources 3.8.4 Output S斟R and SNR 3 9 Multitarget Adaptive Beamformer 3.9.1 Multitarget LS-CMA(MT-LS-CMA) 3.9.2 Signal Classification 3.9.3 Multitarget Decision.directed Algorithm(MT-DDA) 3.10 Least Squares Despread Re-spread Multitarget Array (LS-DRMTA) 3.10.1 LS.DRMTA 3.10.2 LS-DRMT-CMA 3.1 l Adaptive Array Signal Processing Based on Sub-space 3.11.1 Signal Model and Optimal Combination 3.1 1.2 Adaptive Array Algorithm Based OD Sub-space 3.12 Chapter Summary 3.13 Chapter Assignments 4 Space-Time Signal Processing 4.1 Limitations of One-Dimensional Processing 4.1.1 Limitations of One-Dimensional Processing in Time Domain 4.1.2 The Limitation of One-Dimensional Processing in Space Domain 4.2 Discrete Space.Time Channel and Signal Model 4.2.1 Discrete Space-Time Channel Model 4.2.2 Discrete Space-Time Signal Model 4.3 Space.Time M[MSE Receiver 4.3.1 Space.Time MMSE Criterion 4.3.2 Space.Time Equalizer 4.4 Space-Time MLSE Receiver 4.4.1 Space-Time MLSE Criterion 4.4.2 Space-Time MLSE Method 4.5 Space-Time Blind Equalization
4.5.1 Problem Description 4.5.2 Space-Time Blind Equalization 4.5.3 CM Algorithm Based on Weight Vector Updating 4.6 Space-Time Blind Beamforming 4.6.1 FIR MIMO Model of Space-Time Channel 4.6.2 Space-Time Blind Beamforming 4.7 Space-Time Two-Dimensional RAKE Receiver 4.7.1 Signal Model 4.7.2 Space.Time Two.dimensional RAKE Receiver Based on Matched Filter 4.8 Chapter Summary 4.9 Chapter Assignments 5 Summary 5.1 Book Summary 5.2 Prospects References
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