This book provides a complete overview of radar system theory and design for consumer applications, from basic short range radar theory to the integration into the real-world products, focusing particularly on gesture sensing in consumer products. It brings you step-by-step through the theoretical understandings, design procedures, analysis tools, and design examples of radar systems. Packed with practical guidance learned from real consumer product development, the book explains how radar works in plain language; provides design principles applied in consumer products; demonstrates algorithms with real world measurement data; describes system trade-offs and cross-functional solutions for solving integration challenges; equips you to design your own radars in consumer electronics for motion sensing and gesture controls. The book focuses on consumer-oriented radar systems with its theory, specifications, application, challenges in integration and co-existence with other radio components. It is self-contained to cover radar hardware, waveforms/modulations, signal processing, detection and classification, machine learning and UX design. With its unique coverage of consumer-oriented radar systems, coupled with the authors' practical experience in designing radars for today's consumer products, this is a must-have book for engineers and researchers working with radar systems in consumer electronics and mobile devices such as cell phone, wearables, and in the automotive industry. Downloadable MATLAB(R) scripts and Simulink models are included.
Author(s): Jian Wang, Jaime Lien
Series: Artech House Radar Library
Publisher: Artech House
Year: 2022
Language: English
Pages: 254
City: Boston
Motion and Gesture Sensing with Radar
Contents
Preface
1
Introduction
1.1 Radar Basics and Types
1.2 Frequency Bands and Civil Applicatio
1.3 Radar Standardization
1.4 Book Outline
References
2 Radar System Architecture and Range Equation
2.1 Basic Hardware Components of Radar
2.1.1 Transmitter/Receiver (Transceiver)
2.1.2 Waveform Generator
2.1.3 Antennas
2.2 LFM Radar Architecture
2.3 Receiver Noise
2.4 Dynamic Range
2.5 Radar Range Equation
2.6 Radar System Integration
References
3
Radar Signal Model and Demodulation
3.1 Signal Modeling
3.1.1 Point Target
3.1.2 Distributed Target
3.2 Radar Waveforms and Demodulation
3.2.1 Matched Filter
3.2.2 Ambiguity Function
3.3 Frequency Modulated Waveforms
3.3.1 Conventional FMCW Waveforms
3.3.2 LFM Chirp Train (Fast Chirp)
3.3.3 Stretch Processing
3.4 Phase Coded Waveforms
3.4.1 Golay Codes
3.5 Summary
References
4
Radar Signal Processing
4.1 Range Processing (Fast Time Processi
4.1.1 Minimum Range and Maximum Unambigu
4.1.2 Pulse Compression
4.1.3 Range Resolution
4.1.4 Range Accuracy
4.1.5 Time Sidelobes Control
4.2 Doppler Processing (Slow Time Proces
4.2.1 Sampling Frequency in Slow Time Do
4.2.2 CIT Window Size
4.2.3 MTI and Clutter Cancellation
4.2.4 Moving Target Detector (Filter Ban
4.2.5 Doppler (Radial Velocity) Resoluti
4.2.6 Doppler (Radial Velocity) Accuracy
4.2.7 Doppler Sidelobes Control
4.3 Summary
References
5
Array Signal Processing
5.1 Array Manifold and Model
5.2 Conventional Beamforming
5.2.1 Uniform Array and FFT Based Beamfoming
5.2.2 Array Resolution, Accuracy, and Sidelobes Control
5.2.3 Digital Beamforming Versus Analog Beamforming
5.3 High-Resolution Methods
5.4 MIMO
5.4.1 Virtual Array
5.4.2 Basic MIMO Waveforms
5.4.3 Summary
References
6
Motion and Presence Detection
6.1 Introduction
6.2 Detection Theory
6.2.1 Hypothesis Testing and Decision Rules
6.2.2 Neyman-Pearson Criterion and Likelihood Ratio Test
6.3 Signal and Noise Models
6.3.1 Target RCS Fluctuations
6.3.2 Noise
6.4 Threshold Detection
6.4.1 Optimal Detection of Nonfluctuating Target
6.4.2 Detection Performance
6.4.3 Impact of Target Fluctuation
6.5 Constant False Alarm Rate Detection
6.5.1 Cell-Averaging CFAR
6.5.2 Greatest-of and Least-of CFAR
6.5.3 Ordered Statistics CFAR
6.6 Clutter Rejection
6.6.1 Regions of Interest
6.6.2 Doppler Filtering
6.6.3 Spatial Filtering
6.6.4 Adaptive and Machine Learned Clutter Filters
6.7 Interference
6.8 Detection Pipeline Design
References
7
Radar Machine Learning
7.1 Machine Learning Fundamentals
7.1.1 Supervised Learning
7.1.2 Linear Regression
7.1.3 Logistic Regression
7.1.4 Beyond Linear Models
7.1.5 Neural Networks
7.2 Radar Machine Learning
7.2.1 Machine Learning Considerations for Radar
7.2.2 Gesture Classification
7.3 Training, Development, and Testing Datasets
7.4 Evaluation Methodology
7.4.1 Machine Learning Classification Metrics
7.4.2 Classification Metrics for Time Series Data
7.5 The Future of Radar Machine Learning
7.5.1 What’s Next?
7.5.2 Self Supervised Learning
7.5.3 Meta Learning
7.5.4 Sensor Fusion
7.5.5 Radar Standards, Libraries, and Datasets
7.6 Conclusion
References
8
UX Design and Applications
8.1 Overview
8.2 Understanding Radar for Human-Computer Interaction
8.3 A New Interaction Language for Radar Technology
8.3.1 Explicit Interactions: Gestures
8.3.2 Implicit Interactions: Anticipating Users' Behaviors
8.3.3 Movement Primitives
8.4 Use Cases
References
9
Research and Applications
9.1 Technological Trends
9.2 Radar Standardization
9.3 Emerging Applications
References
About the Authors
Index
