Inverse Synthetic Aperture Radar Imaging With MATLAB Algorithms

by Ozdemir, Caner

• ISBN13: 9781119521334
• ISBN10: 1119521335
• Edition: 2nd
• Format: Hardcover
• Copyright: 2021-05-04
• Publisher: Wiley

Summary

Build your knowledge of SAR/ISAR imaging with this comprehensive and insightful resource

The newly revised Second Edition of Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms covers in greater detail the fundamental and advanced topics necessary for a complete understanding of inverse synthetic aperture radar (ISAR) imaging and its concepts. Distinguished author and academician, Caner Özdemir, describes the practical aspects of ISAR imaging and presents illustrative examples of the radar signal processing algorithms used for ISAR imaging. The topics in each chapter are supplemented with MATLAB codes to assist readers in better understanding each of the principles discussed within the book.

This new edition incudes discussions of the most up-to-date topics to arise in the field of ISAR imaging and ISAR hardware design. The book provides a comprehensive analysis of advanced techniques like Fourier-based radar imaging algorithms, and motion compensation techniques along with radar fundamentals for readers new to the subject.

The author covers a wide variety of topics, including:

• Radar fundamentals, including concepts like radar cross section, maximum detectable range, frequency modulated continuous wave, and doppler frequency and pulsed radar
• The theoretical and practical aspects of signal processing algorithms used in ISAR imaging
• The numeric implementation of all necessary algorithms in MATLAB
• ISAR hardware, emerging topics on SAR/ISAR focusing algorithms such as bistatic ISAR imaging, polarimetric ISAR imaging, and near-field ISAR imaging,
• Applications of SAR/ISAR imaging techniques to other radar imaging problems such as thru-the-wall radar imaging and ground-penetrating radar imaging

Perfect for graduate students in the fields of electrical and electronics engineering, electromagnetism, imaging radar, and physics, Inverse Synthetic Aperture Radar Imaging With MATLAB Algorithms also belongs on the bookshelves of practicing researchers in the related areas looking for a useful resource to assist them in their day-to-day professional work.

Author Biography

CANER ÖZDEMIR, PHD, teaches undergraduate and graduate courses on electromagnetics, antennas, radar, and signal processing at Mersin University in Turkey. He has published over 150 scientific journal articles and is the recipient of the URSI EMT-S Young Scientist Award in the 2004 International Symposium on Electromagnetic Theory, as well as the 2016 Best Paper Award in SPIE-Journal of Applied Remote Sensing.

Table of Contents

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