Human hands are natural tools for performing actions and gestures that interact with the physical world. Radar technology allows for touchless wireless gesture sensing by transmitting radio frequency (RF) signals to the target, analyzing the backscattering reflections to extract the target's movements, and thereby accurately detecting gestures for Human Computer Interaction (HCI). A key advantage of this technology is that it allows interaction with machines without any need to attach a sensing device to the hands. Led by researchers from Google's Project Soli, the authors introduce the concept and underpinning technology, cover all design phases, and provide researchers and professionals with the latest advances and innovations in microwave and millimeter wave radar sensing to capture relative movements such as micro gestures.
Author(s): Changzhan Gu Jaime Lien
Series: IET Control, Robotics and Sensors Series, 125
Publisher: The Institution of Engineering and Technology
Year: 2019
Language: English
Pages: 390
City: London
Cover
Contents
1 Introduction
References
2 Proximity RF/microwave biosensor techniques for vital sign detection
2.1 Introduction
2.2 Chest wall sensor
2.2.1 SAW filter system
2.2.2 PLL system
2.2.3 Reflectometry system
2.3 Wrist pulse
2.3.1 Injection-locked PLL
2.3.2 Reflectometry system with array resonator
2.3.3 Interferometry system
2.4 AR method for improved vital sign estimation
2.5 Conclusion
References
3 Wi-Fi-based sensing for gesture control applications
3.1 Introduction
3.2 Injection locking with a modulated signal
3.2.1 Generalized locking equation
3.2.2 Locking range and lock-in time
3.2.3 Frequency pulling
3.2.4 Synchronization
3.2.5 Discrete-time analysis
3.3 Passive radar using Wi-Fi/LTE signals
3.3.1 System architecture
3.3.2 Sensing principle
3.3.3 System performance simulations and verification
3.3.4 Experimental results
3.4 Applications of Wi-Fi-based gesture sensing
3.4.1 Gesture control
3.4.2 Gesture games
3.4.3 Gesture recognition with machine learning
3.4.4 Sensor fusion with camera
3.4.4.1 Vision-based hand gesture detection
3.4.4.2 Sensor-fusion calibration scheme
3.4.4.3 3D Hand trajectories
References
4 Hand gesture recognition based on SIMO Doppler radar sensors
4.1 Doppler radar sensing
4.2 Architecture
4.2.1 Optimal architecture for HGR application
4.2.2 SIMO-structured CW DRS
4.2.3 Experimental implementation of a digital-IF DRS
4.3 Algorithms
4.3.1 Algorithms for the linear retrieval of Doppler signals
4.3.1.1 Dynamic DC offset tracking
4.3.1.2 Linearized arctangent algorithm
4.3.2 Algorithms for HGRs based on a SIMO DRS
4.3.2.1 2-D motion tracking algorithm
4.3.2.2 3-D motion tracking algorithm
4.3.2.3 Motion separation algorithm
4.3.2.4 Localization with a redundant SIMO DRS
4.3.2.5 Micro-Doppler analysis
4.4 Experimental demonstration
4.4.1 Linear retrieval of large-scale 2-D motions
4.4.2 Reconstruction of 2-D gesture patterns
4.4.3 Separation of interfering Doppler signal
4.5 Summary
References
5 FMCW radar systems for short-range micro-motion sensing
5.1 FMCW radar fundamentals
5.2 FMCW radar transceiver
5.2.1 Chirp generator
5.2.2 Coherence
5.2.3 Link budget
5.3 Antenna
5.3.1 Beamforming
5.3.2 Two-way pattern and MIMO
5.4 Radar signal processing
5.4.1 Range profile
5.4.2 Human-aware detection
5.4.3 Range-Doppler imaging
References
6 Noncontact noninvasive monitoring of small laboratory animal's vital sign activities using a 60-GHz radar
6.1 Background
6.1.1 Development of animal experiment
6.1.2 Radar for cardiorespiratory monitoring of laboratory animal
6.2 Radar detection position and body orientation
6.2.1 Radar cross section
6.2.2 Measurements of laboratory rat
6.3 Signal processing for cardiorespiratory movement
6.3.1 Methodology of displacement acquisition
6.3.2 Adaptive harmonics spectrum cancelation for HR
6.4 Conclusion
References
7 Dynamic monopulse radar sensor for indoor positioning and surgical instrument positioning
7.1 Introduction
7.2 Indoor positioning system
7.2.1 Selecting-and-averaging algorithm
7.2.2 2-D positioning concept
7.2.3 System hardware design
7.2.3.1 2-D steerable comparator
7.2.3.2 Circular-polarized antenna array
7.2.3.3 RSSI module and MCU
7.2.3.4 Positioning tag
7.2.4 Multipath interference analysis
7.2.5 Indoor positioning demonstration
7.3 Surgical instrument positioning system
7.3.1 Design consideration
7.3.2 Peak-tracking algorithm
7.3.3 Hardware design
7.3.4 Surgical instrument positioning demonstration
7.4 Conclusion
Acknowledgments
References
8 Noncontact healthy status sensing using low-power digital-IF Doppler radar
8.1 Digital-IF CW Doppler radar
8.1.1 RF layer
8.1.2 IF layer
8.1.3 Baseband layer
8.2 Advanced signal processing algorithms for physiological signal extraction
8.2.1 CS and stepwise ANM
8.2.1.1 Sparse reconstruction
8.2.1.2 MMV model of heartbeat detection
8.2.1.3 Stepwise ANM
8.2.1.4 Laboratorial experiments
8.2.2 SST for instantaneous vital sign detection
8.2.2.1 Synchrosqueezing transform
8.2.2.2 Laboratorial experiments
8.3 Noncontact healthy status sensing
8.3.1 Breathing disorder recognition
8.3.1.1 Breathing disorder recognition module
8.3.1.2 Experiments
8.3.2 Sleep-stage estimation
8.3.2.1 Sleep-stage recognition module
8.3.2.2 Experiments
References
9 Radar measurement of the angular velocity of moving objects
9.1 Radar measurements
9.2 Interferometric measurement of angular velocity
9.3 Measurement resolution and accuracy
9.3.1 Resolution
9.3.2 Accuracy
9.4 Nonlinear distortion and mitigation
9.5 Experimental system examples
9.5.1 Passive 27.4-GHz correlation interferometer system
9.5.2 Active 29.5-GHz dual interferometric-Doppler system
9.6 Conclusions
References
10 Continuous-wave radar sensor for structural displacement monitoring
10.1 Introduction
10.2 Background
10.2.1 Structural health monitoring
10.2.2 Existing displacement sensing technologies
10.2.3 Radar techniques
10.2.3.1 Ground penetrating radar
10.2.3.2 Remote sensing radar
10.2.3.3 Distance measurement radar
10.3 Continuous radar sensor hardware
10.3.1 CW radar system
10.3.1.1 Antenna
10.3.1.2 RF board
10.3.1.3 Baseband board
10.3.1.4 Wireless communication device
10.3.1.5 Power system
10.3.2 AC-coupled radar
10.3.2.1 AC coupling design
10.3.3 DC-coupled radar
10.3.4 Active transponder
10.4 Continuous radar sensor software
10.4.1 Signal-processing algorithms
10.4.1.1 Automated DC tuning process
10.4.1.2 Automated displacement-processing algorithm
10.5 Continuous radar sensor measurement characterization
10.5.1 Dynamic displacement experiments
10.5.2 Static deflection experiments
10.5.3 Moving load experiment
10.5.4 Oblique angle tests
10.6 Continuous radar full-scale structural experiments validation
10.6.1 Sweetwater Park Bridge experiment
10.6.1.1 Sweetwater Park Bridge
10.6.1.2 Instrumentation
10.6.1.3 Description of load conditions
10.6.1.4 Measurement results
10.6.2 Vehicle load experiment
10.7 Conclusions
References
11 Short-distance radar sensing application
11.1 Introduction
11.1.1 Smart healthcare
11.1.2 Biometric authentication
References
12 Micro-Doppler signatures for sensing micro-motion
12.1 An introduction to micro-motion and micro-Doppler effect
12.1.1 Micro-motion and micro-Doppler effect in radar
12.1.2 Micro-Doppler signatures
12.1.2.1 Micro-Doppler signatures of a walking human
12.1.2.2 Micro-Doppler signatures of swimmers
12.1.2.3 Micro-Doppler signatures of hand gestures
12.1.2.4 Micro-Doppler signatures of drones
12.2 Angular velocity-induced micro-Doppler signatures
12.3 Feature extraction and motion decomposition from micro-Doppler signatures
12.3.1 Feature extraction from micro-Doppler signatures
12.3.2 Motion decomposition from micro-Doppler signatures
12.4 Micro-Doppler signature-based identification
12.4.1 Micro-Doppler signature-based classification
12.4.2 Motion identification from micro-Doppler signatures
12.4.3 Classification, recognition, and identification using deep learning neural networks
References
13 Repurposing millimeter-wave communication devices for high-precision wireless sensing
13.1 Introduction
13.2 mTrack: an overview
13.3 Phase-based fine-grained mmWave tracking
13.3.1 Basic successive tracking algorithm
13.3.1.1 Translating phase change into path-length change
13.3.1.2 From distance tracking to 2D tracking
13.3.2 Tracking under background reflection
13.3.2.1 Impact of background reflection on phase shift
13.3.2.2 Phase counting and regeneration
13.4 RSS-based APA
13.4.1 Locating through discrete beam steering
13.4.2 Background RSS subtraction
13.4.3 Opportunistic calibration
13.5 Implementation and evaluation of mTrack
13.5.1 Implementation
13.5.2 Performance on a trackpad
13.5.2.1 Localization/tracking error across a large region
13.5.3 Application of mTrack
13.6 E-Mi: an overview
13.7 Multipath resolution framework
13.7.1 Estimate path angles using phased arrays
13.7.2 Virtual beamforming: match path angles
13.7.2.1 Beam generation
13.7.2.2 Beam matching
13.7.3 Multitone ranging: estimate path length
13.8 Dominant reflector reconstruction
13.8.1 Locating reflecting points in environment
13.8.2 Reconstructing dominant reflector layout and reflectivity
13.8.2.1 Reconstructing dominant reflectors' geometry
13.8.2.2 Estimating reflection loss
13.9 Implementation and evaluation of E-Mi
13.9.1 Implementation
13.9.2 Effectiveness of dominant reflector reconstruction
13.9.2.1 Accuracy in localizing reflecting points
13.9.2.2 Performance of dominant reflector reconstruction
13.9.2.3 Accuracy of link performance prediction
13.9.2.4 Scalability in complicated environment
13.10 Summary
References
14 Conclusion
References
Index
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