The Micro-Doppler Effect in Radar Second Edition
Contents
1
Introduction
1.1 Doppler Effect
1.2 Relativistic Doppler Effect and Time Dilation
1.3 Doppler Effect Observed in Radar
1.4 Estimation and Analysis of Doppler Frequency Shifts
1.5 Cramer-Rao Bound of the Doppler Frequency Estimation
1.6 The Micro-Doppler Effect
1.7 Micro-Doppler Effect Observed in Radar
1.8 Estimation and Analysis of Micro-Doppler Frequency Shifts
1.8.1 Instantaneous Frequency Analysis
1.8.2 Joint Time-Frequency Analysis
1.9 The Micro-Doppler Signature of Objects
1.10 Angular Velocity Induced Interferometric Frequency Shift
1.11 Research and Applications of Radar Micro-Doppler Signatures
1.11.1 Micro-Doppler Signatures of Space Targets
1.11.2 Micro-Doppler Signatures of Air Targets
1.11.3 Micro-Doppler Signatures of Vital Signs
1.11.4 Through-the-Wall Radar Micro-Doppler Signatures
1.11.5 Micro-Doppler Signatures for Indoor Monitoring
1.11.6 Micro-Doppler Signatures for Hand Gesture Recognition
1.11.7 Micro-Doppler Signatures for Target Classification
1.11.8 Other Applications of Radar Micro-Doppler Signatures
1.12 The Organization of the Book
References
2
The Basics of the Micro-Doppler Effect in Radar
2.1 Rigid Body Motion
2.1.1 Euler Angles
2.1.2 Quaternion
2.1.3 Equations of Motion
2.2 Nonrigid Body Motion
2.3 Electromagnetic Scattering from a Body with Motion
2.3.1 Radar Cross Section of a Target
2.3.2 RCS Prediction Methods
2.3.3 EM Scattering from a Body with Motion
2.4 Basic Mathematics for Calculating the Micro-Doppler Effect
2.4.1 Micro-Doppler Induced by a Target with Micromotion
2.4.2 Vibration-Induced Micro-Doppler Shift
2.4.3 Rotation-Induced Micro-Doppler Shift
2.4.4 Coning Motion-Induced Micro-Doppler Shift
2.5 Bistatic Micro-Doppler Effect
2.6 Multistatic Micro-Doppler Effect
2.7 Cramer-Rao Bound of the Micro-Doppler Estimation
References
Appendix 2A
3
The Micro-Doppler Effect of the Rigid Body Motion
3.1 Pendulum Oscillation
3.1.1 Modeling Nonlinear Motion Dynamic of a Pendulum
3.1.2 Modeling RCS of a Pendulum
3.1.3 Radar Backscattering from an Oscillating Pendulum
3.1.4 Micro-Doppler Signatures Generated by an Oscillating Pendulum
3.2 Helicopter Rotor Blades
3.2.1 Mathematic Model of Rotating Rotor Blades
3.2.2 RCS Model of Rotating Rotor Blades
3.2.3 POFACET Prediction Model
3.2.4 Radar Backscattering from Rotor Blades
3.2.5 Micro-Doppler Signatures of Rotor Blades
3.2.6 Required Minimum PRF
3.2.7 Analysis and Interpretation of the Micro-Doppler Signature of Rotor Blades
3.2.8 Quadrotor and Multirotor Unmanned Aerial Vehicles
3.3 Spinning Symmetric Top
3.3.1 Force-Free Rotation of a Symmetric Top
3.3.2 Torque-Induced Rotation of a Symmetric Top
3.3.3 RCS Model of a Symmetric Top
3.3.4 Radar Backscattering from a Symmetric Top
3.3.5 Micro-Doppler Signatures Generated by a Precessing Top
3.3.6 Analysis and Interpretation of the Micro-Doppler Signature of a Precessing Top
3.4 Micro-Doppler Signatures of Re-Entry Vehicles
3.4.1 Mathematical Model of a Cone-Shaped RV
3.4.2 Motion Dynamic Model of a Cone-Shaped RV
3.4.3 Micro-Doppler Signature Analysis
3.4.4 Summary
3.5 Wind Turbines
3.5.1 Micro-Doppler Signatures of Wind Turbines
3.5.2 Analysis and Interpretation of the Micro-Doppler Signature of Wind Turbines
3.5.3 Simulation Study on Wind Turbines
References
4
The Micro-Doppler Effect of the Nonrigid Body Motion
4.1 Human Body Articulated Motion
4.1.1 Human Walking
4.1.2 Description of Periodic Motion of Human Walking
4.1.3 Simulation of Human Body Movements
4.1.4 Human Body Segment Parameters
4.1.5 Human Walking Model Derived from Empirical Mathematical Parameterizations
4.1.6 Capturing Human Motion Kinematic Parameters
4.1.7 Three-Dimensional Kinematic Data Collection
4.1.8 Characteristics of Angular Kinematics Using the Angle-Cyclogram Pattern
4.1.9 Radar Backscattering from a Walking Human
4.1.10 Human Body Movement Data Processing
4.1.11 Human Body Movements-Induced Radar Micro-Doppler Signatures
4.1.12 Motion Captured Data for Human Activities
4.2 Bird Wing Flapping
4.2.1 Bird Wing Flapping Kinematics
4.2.2 Doppler Observations of the Bird Wing Flapping
4.2.3 Simulation of the Bird Wing Flapping
4.3 Quadrupedal Animal Motion
4.3.1 Modeling of Quadrupedal Locomotion
4.3.2 Micro-Doppler Signatures of Quadrupedal Locomotion
4.3.3 Summary
References
5
Application to Vital Sign Detection
5.1 Vibrating Surface Modeling of Vital Signs
5.2 Homodyne Doppler Radar Systems for Vital Sign Detection
5.2.1 Homodyne Receivers for Vital Sign Detection
5.2.2 Homodyne Receivers with Quadrature Mixer
5.3 Heterodyne Doppler Radar Systems for Vital Sign Detection
5.3.1 Double-Sideband Mixer and Single-Sideband Mixer
5.3.2 The Low-IF Architecture
5.4 Experimental Doppler Radar for Vital Sign Detection
References
6
Application to Hand Gesture Recognition
6.1 Modeling of Hand and Finger Movement
6.2 Capturing of Hand and Finger Movements
6.2.1 Traditional Motion Capture Methods
6.2.2 Acoustic Doppler-Based Systems for Hand Gesture Recognition
6.2.3 Radar Doppler-Based Systems for Hand Gesture Recognition
6.3 Radar Micro-Doppler Signatures for Hand Gesture Recognition
6.4 Other Features for Hand Gesture Recognition
6.4.1 Time-Varying Range-Doppler Features
6.4.2 Azimuth and Elevation Angle Features
6.4.3 Fine-Grained Hand Gesture Recognition
6.4.4 Radar Frontal Imaging of Hand Gestures
References
7
Overview of the Micro-Doppler Radar System
7.1 Micro-Doppler Radar System Architecture
7.2 Signal Waveforms for the Micro-Doppler Radar System
7.3 Resolution and Range Coverage
7.4 Radar Range Equation
7.4.1 CW Radar Range Equation
7.4.2 Receive Noise Floor
7.4.3 The Required Signal Level
7.4.4 Received Signal Power
7.4.5 Receiver Sensitivity
7.4.6 Receiver Dynamic Range
7.4.7 Maximum Detection Range
7.5 Data Acquisition and Signal Processing
7.5.1 Noise Sources
7.5.2 Digital Data Acquisition
7.5.3 Signal Conditioning
7.5.4 In-Phase and Quadrature Imbalance and Its Compensation
References
8
Analysis and Interpretation of Micro-Doppler Signatures
8.1 Biological Motion Perception
8.2 Decomposition of Biological Motion
8.2.1 Statistics-Based Decomposition
8.2.2 Decomposition of Micro-Doppler Signatures in the Joint Time-Frequency Domain
8.2.3 Physical Component-Based Decomposition
8.3 Extraction of Features from Micro-Doppler Signatures
8.4 Estimation of Kinematic Parameters from Micro-Doppler Signatures
8.5 Identifying Human Body Movements
8.5.1 Features Used for Identifying Human Body Movements
8.5.2 Anomalous Human Behavior
8.6 Summary
References
9
Summary, Challenges, and Perspectives
9.1 Summary
9.2 Challenges
9.2.1 Decomposing Micro-Doppler Signatures
9.2.2 Feature Extraction and Kinematic Parameter Estimation from Micro-Doppler Signatures
9.3 Perspectives
9.3.1 Multistatic Micro-Doppler Analysis
9.3.2 Micro-Doppler Signature-Based Classification, Recognition and Identification
9.3.3 Deep Learning for Micro-Doppler Signature-Based Classification, Recognition, and Identification
9.3.4 Aural Methods for Micro-Doppler-Based Discrimination
9.3.5 Through-the-Wall Micro-Doppler Signatures
9.3.6 Micro-Doppler Signatures for Detection of Targets in Sea Clutter
References
About the Author
Index