Springer Information Sciences

1. Overview.- I Detection and Estimation.- 2. Radar Detection Using Array Processing.- 2.1 Observation Model.- 2.2 Coherent Radar Detection.- 2.2.1 Signal and Noise Model.- 2.2.2 Detection of Targets with Known Directions.- 2.2.3 Detection of Targets with Unknown Directions.- 2.3 Noncoherent Radar Detection.- 2.3.1 Signal and Noise Model.- 2.3.2 Detection of Targets with Known Directions.- 2.3.3 Detection of Targets with Unknown Directions: Deterministic Signal.- 2.3.4 Detection of Targets with Unknown Directions: Gaussian Signal.- 2.4 Passive Radar Detection.- 2.4.1 Signal and Noise Model.- 2.4.2 Detection of Emitters with Known Directions.- 2.4.3 Detection of Emitters with Unknown Directions: Deterministic Signal.- 2.4.4 Detection of Emitters with Unknown Directions: Gaussian Signal.- 2.5 Discussion.- References.- Additional References.- 3. Radar Target Parameter Estimation with Array Antennas.- 3.1 Radar Parameter Estimation Problem.- 3.1.1 Range and Angle Estimation.- 3.1.2 Frequency and Power Estimation.- 3.2 Angle Estimation.- 3.2.1 Monopulse Estimation (Single Target Estimation).- 3.2.2 Covariance Matrix Estimation.- 3.2.3 Linear Prediction Methods.- 3.2.4 Capon-Pisarenko-Type Methods.- 3.2.5 Signal Subspace Methods.- 3.2.6 Parametric Target Model Fitting.- 3.2.7 Aspects of Implementation.- 3.3 Frequency Estimation.- 3.3.1 Doppler Filter Bank.- 3.3.2 Superresolution Methods.- 3.4 Range, Amplitude and Power Estimation.- 3.4.1 Conventional Range Estimation.- 3.4.2 Superresolution in Range.- 3.4.3 Amplitude and Power Estimation.- 3.5 Summary.- References.- 4. Exact and Large Sample Maximum Likelihood Techniques for Parameter Estimation and Detection in Array Processing.- 4.1 Background.- 4.2 Chapter Outline.- 4.3 Sensor Array Processing.- 4.3.1 Narrowband Data Model.- 4.3.2 Parametric Data Model.- 4.3.3 Assumptions and Problem Formulation.- 4.3.4 Parameter Identifiability.- 4.4 Exact Maximum Likelihood Estimation.- 4.4.1 Stochastic Maximum Likelihood Method.- 4.4.2 Deterministic Maximum Likelihood Method.- 4.4.3 Bounds of Estimation Accuracy.- 4.4.4 Asymptotic Properties of Maximum Likelihood Estimates.- 4.4.5 Order Relations.- 4.5 Large Sample Maximum Likelihood Approximations.- 4.5.1 Subspace Based Approach.- 4.5.2 Relation Between Subspace Formulations.- 4.5.3 Relation to Maximum Likelihood Estimation.- 4.6 Calculating the Estimates.- 4.6.1 Newton-Type Search Algorithms.- 4.6.2 Gradients and Approximate Hessians.- 4.6.3 Uniform Linear Arrays.- 4.6.4 Practical Aspects.- 4.7 Detection of Coherent/Noncoherent Signals.- 4.7.1 Generalized Likelihood Ratio Test Based Detection.- 4.7.2 Subspace Based Detection.- 4.8 Numerical Examples and Simulations.- 4.9 Conclusions.- Appendix 4.A Differentiation of the Projection Matrix.- Appendix 4.B Asymptotic Distribution of the Weighted Subspace Fitting Criterion.- References.- II Systolic Arrays.- 5. Systolic Adaptive Beamforming.- 5.1 Adaptive Antenna Arrays.- 5.2 Systolic and Wavefront Arrays.- 5.3 Canonical Problem.- 5.3.1 Canonical Configuration.- 5.3.2 Least-Squares Formulation.- 5.4 QR Decomposition by Givens Rotations.- 5.4.1 QR Decomposition.- 5.4.2 Givens Rotations.- 5.4.3 Systolic Array Implementation.- 5.4.4 Square-Root-Free Algorithm.- 5.4.5 Sensitivity to Arithmetic Precision.- 5.5 Direct Residual Extraction.- 5.5.1 Definition of Residuals.- 5.5.2 Properties of Rotation Matrix Q?(n).- 5.5.3 A Posteriori Residual Extraction.- 5.5.4 A Priori Residual Extraction.- 5.6 Weight Freezing and Flushing.- 5.6.1 Basic Concept.- 5.6.2 Frozen Networks.- 5.6.3 Serial Weight Flushing.- 5.6.4 Further Insights.- 5.7 Linear Constraint Pre-Processor.- 5.7.1 Single Constraint Pre-Processor.- 5.7.2 Multiple Constraint Pre-Processor.- 5.7.3 Generalized Sidelobe Canceller.- 5.8 Minimum Variance Distortionless Response Beamforming.- 5.8.1 Schreiber's Algorithm.- 5.8.2 SystoUc Array Implementation.- 5.8.3 Square-Root-Free Minimum Variance Distortionless Response Algorithm.- 5.9 Adaptive Antenna Processor Test-Bed.- 5.9.1 Wavefront Array Processor.- 5.10 Further Developments.- 5.10.1 Parallel Weight Extraction.- 5.10.2 QR-with-Feedback.- 5.10.3 Structures for Broad-Band Adaptive Beamforming.- 5.10.4 QR Decomposition and Neural Networks.- 5.11 Comments and Conclusions.- 5.11.1 Additional Topics.- Appendix 5.A Modified Gram-Schmidt Algorithm.- Appendix 5.B Constraints with Leading Zeros.- Appendix 5.C Weighted Least-Squares and Hyperbolic Rotations.- Appendix 5.D Principal Symbols Used in this Chapter.- References.- 6. Two-Dimensional Adaptive Beamforming: Algorithms and Their Implementation.- 6.1 Arrangement of the Chapter.- 6.2 Adaptive Beamforming Techniques.- 6.2.1 Introduction.- 6.2.2 Classical Adaptive Beamforming.- 6.2.3 Modern Adaptive Beamforming.- 6.3 2D Adaptive Beamforming Algorithm and Implementation.- 6.3.1 Introduction.- 6.3.2 Classical Approaches.- 6.3.3 2D QRD-LS Algorithm and Systolic Array Implementation.- 6.4 Simulation Results.- 6.5 Concluding Remarks.- References.- III Imaging.- 7. The Radio Camera.- 7.1 Problems.- 7.2 Adaptive Beamforming: Dominant Scatterer Algorithm.- 7.3 Experiments.- 7.4 Adaptive Beamforming: Spatial Correlation Algorithm.- 7.5 Experiments.- 7.6 Number of Elements.- 7.7 Number of Bits per Sample.- 7.8 Data Truncation.- 7.9 Conclusions.- References.