Short-Range Micro-Motion Sensing with Radar Technology
- Length: 392 pages
- Edition: 1
- Language: English
- Publisher: The Institution of Engineering and Technology
- Publication Date: 2019-08-21
- ISBN-10: 1785617605
- ISBN-13: 9781785617607
- Sales Rank: #5951426 (See Top 100 Books)
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.
Cover Title Copyright 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 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 4.3.2 Algorithms for HGRs based on a SIMO DRS 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 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 7.2.4 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 8.2.2 SST for instantaneous vital sign detection 8.3 Noncontact healthy status sensing 8.3.1 Breathing disorder recognition 8.3.2 Sleep-stage estimation 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 10.3 Continuous radar sensor hardware 10.3.1 CW radar system 10.3.2 AC-coupled radar 10.3.3 DC-coupled radar 10.3.4 Active transponder 10.4 Continuous radar sensor software 10.4.1 Signal-processing algorithms 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 10.6.2 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 12.2 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 13.3.2 Tracking under background reflection 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 13.5.3 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 13.7.3 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 13.9 Implementation and evaluation of E-Mi 13.9.1 Implementation 13.9.2 Effectiveness of dominant reflector reconstruction 13.10 Summary References 14 Conclusion References Index
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