Inverse synthetic aperture radar imaging with MATLAB algorithms : with advanced sar/isar imaging concepts, algorithms, and matlab codes

목차 (Table of Contents)

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• Preface to the Second Edition Acknowledgments Acronyms 1 Basics of Fourier Analysis 1.1 Forward and Inverse Fourier Transform 1.1.1 Brief History of Fourier Transform 1.1.2 Forward FT Operation 1.1.3 IFT 1.2 FT Rules and Pairs 1.2.1 Linearity 1.2.2 Time Shifting 1.2.3 Frequency Shifting 1.2.4 Scaling 1.2.5 Duality 1.2.6 Time Reversal 1.2.7 Conjugation 1.2.8 Multiplication 1.2.9 Convolution 1.2.10 Modulation 1.2.11 Derivation and Integration 1.2.12 Parseval’s Relationship 1.3 Time-Frequency Representation of a Signal 1.3.1 Signal in the Time Domain 1.3.2 Signal in the Frequency Domain 1.3.3 Signal in the JFT Plane 1.4 Convolution and Multiplication Using FT 1.5 Filtering/Windowing 1.6 Data Sampling 1.7 DFT and FFT 1.7.1 DFT 1.7.2 FFT 1.7.3 Bandwidth and Resolutions 1.8 Aliasing 1.9 Importance of Fourier Transform in Radar Imaging 1.10 Effect of Aliasing in Radar Imaging 1.11 Matlab Codes References 2 Radar Fundamentals 2.1 Electromagnetic (EM) Scattering 2.2 Scattering from PECs 2.3 Radar Cross Section (RCS) 2.3.1 Definition of RCS 2.3.2 RCS of Simple Shaped Objects 2.3.3 RCS of Complex Shaped Objects 2.4 Radar Range Equation 2.4.1 Bistatic Case 2.4.2 Monostatic Case 2.5 Range of Radar Detection 2.5.1 Signal-to-Noise Ratio (SNR) 2.6 Radar Waveforms 2.6.1 CW 2.6.2 FMCW 2.6.3 SFCW 2.6.4 Short Pulse 2.6.5 Chirp (LFM) Pulse 2.7 Pulsed Radar 2.7.1 PRF 2.7.2 Maximum Range and Range Ambiguity 2.7.3 Doppler Frequency 2.8 Matlab Codes References 3 Synthetic Aperture Radar 3.1 SAR Modes 3.2 SAR System Design 3.3 Resolutions in SAR 3.4 SAR Image Formation 3.5 Range Compression 3.5.1 Matched Filter 3.5.2 Ambiguity Function 3.5.2.1 Relation to matched filter 3.5.2.2 Ideal ambiguity function 3.5.2.3 Rectangular-pulse ambiguity function 3.5.2.4 LFM-pulse ambiguity function 3.5.3 Pulse compression 3.5.3.1 Detailed processing of pulse compression 3.5.3.2 Bandwidth, Resolution, and Compression Issues for LFM signal  3.5.3.3 Pulse compression example 3.6 Azimuth Compression 3.6.1 Processing in Azimuth 3.6.2 Azimuth Resolution 3.6.3 Relation to ISAR 3.7 SAR Imaging 3.8 SAR Focusing Algorithms 3.8.1 RDA 3.8.1.1 Range compression in RDA 3.8.1.1.1 Matched filtering 3.8.1.1.2 Received raw SAR data 3.8.1.1.3 Range compression using matched filtering 3.8.1.2 Azimuth Fourier transform 3.8.1.3 Range Cell Migration Correction 3.8.1.4 Azimuth compression 3.8.1.5 Simulated SAR imaging example 3.8.1.6 Drawbacks of RDA 3.8.2 Chirp Scaling Algorithm 3.9.3 The ω-kA 3.9.4 Back Projection Algorithm 3.10 Example of a real SAR imagery 3.11 Problems in SAR Imaging 3.11.1 Range Migration and range walk 3.11.2 Motion Errors 3.11.3 Speckle Noise 3.12 Advanced Topics in SAR 3.12.1 SAR Interferometry 3.12.2 SAR Polarimetry 3.13 Matlab Codes References 4 Inverse Synthetic Aperture Radar Imaging and Its Basic Concepts 4.1 SAR versus ISAR 4.2 The Relation of Scattered Field to the Image Function in ISAR 4.3 One-Dimensional (1D) Range Profile 4.4 1D Cross-Range Profile 4.5 Two-Dimensional (2D) ISAR Image Formation (Small Bandwidth, Small Angle) 4.5.1 Resolutions in ISAR 4.5.1.1 Range resolution 4.5.1.2 The Cross-range resolution 4.5.2 Range and Cross-Range Extends 4.5.3 Imaging Multi-Bounces in ISAR 4.5.4 Sample Design Procedure for ISAR 4.5.4.1 ISAR Design Example # 1: “Aircraft target” 4.5.4.2 ISAR Design Example # 2: “Military tank target” 4.6 2D ISAR Image Formation (Wide Bandwidth, Large Angles) 4.6.1 Direct Integration 4.6.2 Polar Reformatting 4.7 3D ISAR Image Formation 4.7.1 Range and Cross-Range Resolutions 4.7.2 A Design Example 4.8 Matlab Codes References 5 Imaging Issues in Inverse Synthetic Aperture Radar 5.1 Fourier-Related Issues 5.1.1 DFT Revisited 5.1.2 Positive and Negative Frequencies in DFT 5.2 Image Aliasing 5.3 Polar Reformatting Revisited 5.3.1 Nearest Neighbor Interpolation 5.3.2 Bilinear Interpolation 5.4 Zero Padding 5.5 Point Spread Function (PSF) 5.6 Windowing 5.6.1 Common Windowing Functions 5.6.1.1 Rectangular Window 5.6.1.2 Triangular Window 5.6.1.3 Hanning Window 5.6.1.4 Hamming Window 5.6.1.5 Kaiser Window 5.6.1.6 Blackman Window 5.6.1.7 Chebyshev Window 5.6.2 ISAR Image Smoothing via Windowing 5.7 Matlab Codes References 6 Range-Doppler Inverse Synthetic Aperture Radar Processing 6.1 Scenarios for ISAR 6.1.1 Imaging Aerial Targets via Ground-Based Radar 6.1.2 Imaging Ground/Sea Targets via Aerial Radar 6.2 ISAR Waveforms for Range-Doppler Processing 6.2.1 Chirp Pulse Train 6.2.2 Stepped Frequency Pulse Train 6.3 Doppler Shift’s Relation to Cross Range 6.3.1 Doppler Frequency Shift Resolution 6.3.2 Resolving Doppler Shift and Cross Range 6.4 Forming the Range-Doppler Image 6.5 ISAR Receiver 6.5.1 ISAR Receiver for Chirp Pulse Radar 6.5.2 ISAR Receiver for SFCW Radar 6.6 Quadrature Detection 6.6.1 I-Channel Processing 6.6.2 Q-Channel Processing 6.7 Range Alignment 6.8 Defining the Range-Doppler ISAR Imaging Parameters 6.8.1 Image Frame Dimension (Image Extends) 6.8.2 Range–Cross-Range Resolution 6.8.3 Frequency Bandwidth and the Center Frequency 6.8.4 Doppler Frequency Bandwidth 6.8.5 PRF 6.8.6 Coherent Integration (Dwell) Time 6.8.7 Pulse Width 6.9 Example of Chirp Pulse-Based Range-Doppler ISAR Imaging 6.10 Example of SFCW-Based Range-Doppler ISAR Imaging 6.11 Matlab Codes References 7 Scattering Center Representation of Inverse Synthetic Aperture Radar 7.1 Scattering/Radiation Center Model 7.2 Extraction of Scattering Centers 7.2.1 Image Domain Formulation 7.2.1.1 Extraction in the Image Domain: The “CLEAN” algorithm 7.2.1.2 Reconstruction in the Image Domain 7.2.2 Fourier Domain Formulation 7.2.2.1 Extraction in the Fourier Domain 7.2.2.2 Reconstruction in the Fourier Domain 7.3 Matlab Codes References 8 Motion Compensation for Inverse Synthetic Aperture Radar 8.1 Doppler Effect Due to Target Motion 8.2 Standard MOCOMP Procedures 8.2.1 Translational MOCOMP 8.2.1.1 Range Tracking 8.2.1.2 Doppler Tracking 8.2.2 Rotational MOCOMP 8.3 Popular MOCOMP Techniques in ISAR 8.3.1 Cross-Correlation Method 8.3.1.1 Example for the Cross-Correlation Method 8.3.2 Minimum Entropy Method 8.3.2.1 Definition of Entropy in ISAR Images 8.3.2.2 Example for the Minimum Entropy Method 8.3.3 JFT-Based MOCOMP 8.3.3.1 Received Signal from a Moving Target 8.3.3.2 An Algorithm for JTF-Based Rotational MOCOMP 8.3.3.3 Example for JTF-Based Rotational MOCOMP 8.3.4 Algorithm for JTF-Based Translational and Rotational MOCOMP 8.3.4.1 A Numerical Example 8.4 Matlab Codes References 9. Bistatic ISAR Imaging 9.1 Why Bi-ISAR Imaging? 9.2 Geometry for Bi-ISAR Imaging and the Algorithm 9.2.1 Bi-ISAR Imaging algorithm for a point scatterer 9.2.2 Bistatic ISAR Imaging algorithm for a target 9.3 Resolutions in Bistatic ISAR 9.3.1 Range resolution 9.3.2 Cross-range resolution 9.3.3 Range and cross-range extends 9.4 Design Procedure for Bi-ISAR Imaging 9.5 Bi-ISAR Imaging Examples 9.5.1 Bi-ISAR design example #1 9.5.1 Bi-ISAR design example #2 9.6 Mu-ISAR Imaging 9.6.1 Challenges in Mu-ISAR imaging 9.6.2 Mu-ISAR Imaging Example 9.7. Matlab Codes References 10. Polarimetric ISAR Imaging 10.1 Polarization of an Electromagnetic (EM) Wave 10.1.1 Polarization Type 10.1.2 Polarization Sensitivity 10.1.3 Polarization in Radar Systems 10.2 Polarization Scattering Matrix 10.2.1 Relation to RCS 10.2.2 Polarization Characteristics of the Scattered Wave 10.2.3 Polarimetric decompositions of EM wave scattering 10.2.4 The Pauli decomposition 10.2.4.1 Description of Pauli decomposition 10.2.4.2 Interpretation of Pauli decomposition 10.2.4.3 Polarimetric Image Representation using Pauli decomposition 10.3 Why Polarimetric ISAR Imaging? 10.4 ISAR Imaging with Full Polarization 10.4.1 ISAR data in LP basis 10.4.2 ISAR data in CP basis 10.5. Polarimetric ISAR (Pol-ISAR) Images 10.5.1 Pol-ISAR image of a benchmark target 10.5.1.1 The “SLICY” target 10.5.1.2 Fully polarimetric EM simulation of SLICY 10.5.1.3 LP Pol-ISAR images of SLICY 10.5.1.4 CP Pol-ISAR images of SLICY 10.5.1.5 Pauli decomposition image of SLICY 10.5.2 Pol-ISAR image of a complex target 10.5.2.1 The “Military Tank” Target 10.5.2.2 Fully polarimetric EM simulation of “Tank” target 10.5.2.3 LP Pol-ISAR images of “Tank” target 10.5.3.4 CP Pol-ISAR images of “Tank” target 10.5.3.5 Pauli decomposition image of “Tank” target 10.6. Feature Extraction from Polarimetric Images 10.7. Matlab Codes References 11. Near-field ISAR imaging 11.1 Definitions of Far and Near-Field Regions 11.1.1 The far-field region 11.1.1.1 The far-field definition based on target’s cross-range extend 11.1.1.2 The far-field definition based on target’s range extend 11.1.2 The Near-field region 11.2 Near-Field Signal Model for the Back-Scattered Field 11.3 Near-Field ISAR Imaging Algorithms 11.3.1 “Focusing operator” algorithm 11.3.2 Back-projection algorithm 11.3.2.1 Fourier Slice Theorem 11.3.2.2 BPA Formulation (3D case) 11.3.2.3 BPA Formulation (2D case) 11.4 Data Sampling Criteria and the Resolutions 11.5 Near-Field ISAR Imaging Examples 11.5.1 Point scatterers in the near-field: Comparison of far- and near-field imaging algorithms 11.5.2 Near-field ISAR imaging of a large object 11.5.3 Near-field ISAR imaging of a small object 11.6 Matlab Codes References 12 Examples of Applications Based on SAR/ISAR 12.1. Imaging subsurface objects: GPR-SAR 12.1.1. The GPR problem 12.1.2 B-Scan GPR in Comparison to Strip-map SAR 12.1.3 Focused GPR images using SAR  12.1.3.1 GPR Focusing with ω-k algorithm (ω-kA) 12.1.3.2 GPR Focusing with BPA 12.1.3.3 Other popular GPR focusing techniques 12.2. Through-the-wall radar imaging (TWIR) using SAR 12.2.1 Challenges in TWIR 12.2.2 Techniques to improve cross-range resolution in TWIR 12.2.3 The use of SAR in TWIR 12.2.4 Example of SAR-based TWIR 12.3 Imaging Antenna-Platform Scattering: ASAR 12.3.1 The ASAR Imaging Algorithm 12.3.2 Numerical Example for ASAR imagery 12.4. Imaging platform coupling between antennas: ACSAR 12.4.1 The ACSAR imaging algorithm 12.4.2 Numerical Example for ACSAR 12.4.3. Applying ACSAR concept to the GPR problem References Appendix Index