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Short-Range Micro-Motion Sensing with Radar Technology [Kõva köide]

Edited by (Shanghai Jiao Tong University, China), Edited by (Google Advanced Technology and Projects (ATAP), USA)
  • Formaat: Hardback, 392 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 21-Aug-2019
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785617605
  • ISBN-13: 9781785617607
Teised raamatud teemal:
Short-Range Micro-Motion Sensing with Radar TechnologySuurem pilt
  • Formaat: Hardback, 392 pages, kõrgus x laius: 234x156 mm
  • Sari: Control, Robotics and Sensors
  • Ilmumisaeg: 21-Aug-2019
  • Kirjastus: Institution of Engineering and Technology
  • ISBN-10: 1785617605
  • ISBN-13: 9781785617607
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781785617607.html
Märksõnad:

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.



Covering radar sensor hardware, digital signal processing and machine learning, the book provides researchers and practitioners with insights into the latest advancements in the field.

1 Introduction
1(6)
Changzhan Gu
Jaime Lien
References
4(3)
2 Proximity RF/microwave biosensor techniques for vital sign detection
7(38)
Hyunjae Lee
Byung-Hyun Kim
Sang-Gyu Kim
Yunseog Hong
Gi-Ho Yun
Hee-Jo Lee
Yong-Jun An
Jong-Gwan Yook
2.1 Introduction
7(1)
2.2 Chest wall sensor
8(17)
2.2.1 SAW filter system
8(4)
2.2.2 PLL system
12(5)
2.2.3 Reflectometry system
17(8)
2.3 Wrist pulse
25(13)
2.3.1 Injection-locked PLL
25(6)
2.3.2 Reflectometry system with array resonator
31(6)
2.3.3 Interferometry system
37(1)
2.4 AR method for improved vital sign estimation
38(2)
2.5 Conclusion
40(1)
References
41(4)
3 Wi-Fi-based sensing for gesture control applications
45(44)
Mu-Cyan Tang
Yi-Chen Lai
Min-Hui Lin
Chien-Lun Chen
Chuan-Chi Chou
Fu-Kang Wang
Chia-Hung Yeh
Chien-Jung Li
Tzyy-Sheng Jason Horng
3.1 Introduction
45(2)
3.2 Injection locking with a modulated signal
47(9)
3.2.1 Generalized locking equation
47(3)
3.2.2 Locking range and lock-in time
50(1)
3.2.3 Frequency pulling
51(1)
3.2.4 Synchronization
52(2)
3.2.5 Discrete-time analysis
54(2)
3.3 Passive radar using Wi-Fi/LTE signals
56(15)
3.3.1 System architecture
56(2)
3.3.2 Sensing principle
58(2)
3.3.3 System performance simulations and verification
60(6)
3.3.4 Experimental results
66(5)
3.4 Applications of Wi-Fi-based gesture sensing
71(13)
3.4.1 Gesture control
71(1)
3.4.2 Gesture games
72(2)
3.4.3 Gesture recognition with machine learning
74(2)
3.4.4 Sensor fusion with camera
76(8)
References
84(5)
4 Hand gesture recognition based on SIMO Doppler radar sensors
89(28)
Yi Zhang
Shuqing Dong
Chengkai Zhu
Anjie Zhu
Zhitao Gu
Tenlong Fan
Qinyi Lv
Jingyu Wang
Changzhan Gu
Lixin Ran
4.1 Doppler radar sensing
89(2)
4.2 Architecture
91(4)
4.2.1 Optimal architecture for HGR application
91(2)
4.2.2 SIMO-structured CW DRS
93(1)
4.2.3 Experimental implementation of a digital-IF DRS
94(1)
4.3 Algorithms
95(12)
4.3.1 Algorithms for the linear retrieval of Doppler signals
95(4)
4.3.2 Algorithms for HGRs based on a SIMO DRS
99(8)
4.4 Experimental demonstration
107(6)
4.4.1 Linear retrieval of large-scale 2-D motions
108(1)
4.4.2 Reconstruction of 2-D gesture patterns
109(2)
4.4.3 Separation of interfering Doppler signal
111(2)
4.5 Summary
113(1)
References
113(4)
5 FMCW radar systems for short-range micro-motion sensing
117(28)
Zhengyu Peng
Changzhi Li
Roberto Gomez-Garcia
Jose-Maria Munoz-Ferr eras
5.1 FMCW radar fundamentals
118(4)
5.2 FMCW radar transceiver
122(4)
5.2.1 Chirp generator
122(1)
5.2.2 Coherence
123(2)
5.2.3 Link budget
125(1)
5.3 Antenna
126(8)
5.3.1 Beamforming
127(6)
5.3.2 Two-way pattern and MIMO
133(1)
5.4 Radar signal processing
134(7)
5.4.1 Range profile
135(2)
5.4.2 Human-aware detection
137(2)
5.4.3 Range-Doppler imaging
139(2)
References
141(4)
6 Noncontact noninvasive monitoring of small laboratory animal's vital sign activities using a 60-GHz radar
145(26)
Tien-Yu Huang
Linda Hayward
Jenshan Lin
6.1 Background
145(2)
6.1.1 Development of animal experiment
145(1)
6.1.2 Radar for cardiorespiratory monitoring of laboratory animal
146(1)
6.2 Radar detection position and body orientation
147(10)
6.2.1 Radar cross section
147(2)
6.2.2 Measurements of laboratory rat
149(8)
6.3 Signal processing for cardiorespiratory movement
157(9)
6.3.1 Methodology of displacement acquisition
157(5)
6.3.2 Adaptive harmonics spectrum cancelation for HR
162(4)
6.4 Conclusion
166(1)
References
167(4)
7 Dynamic monopulse radar sensor for indoor positioning and surgical instrument positioning
171(26)
Chia-Chan Chang
Jen-Chieh Wu
Yen-Chih Chang
Peng-Yu Chen
Sheng-Fuh Chang
7.1 Introduction
171(2)
7.2 Indoor positioning system
173(11)
7.2.1 Selecting-and-averaging algorithm
173(3)
7.2.2 2-D positioning concept
176(2)
7.2.3 System hardware design
178(1)
7.2.4 Multipath interference analysis
179(4)
7.2.5 Indoor positioning demonstration
183(1)
7.3 Surgical instrument positioning system
184(7)
7.3.1 Design consideration
185(1)
7.3.2 Peak-tracking algorithm
185(4)
7.3.3 Hardware design
189(1)
7.3.4 Surgical instrument positioning demonstration
189(2)
7.4 Conclusion
191(1)
Acknowledgments
191(1)
References
192(5)
8 Noncontact healthy status sensing using low-power digital-IF Doppler radar
197(22)
Hong Hong
Heng Zhao
Li Zhang
Chuanwei Ding
Xiaohua Zhu
8.1 Digital-IF CW Doppler radar
197(5)
8.1.1 RF layer
198(1)
8.1.2 IF layer
199(2)
8.1.3 Baseband layer
201(1)
8.2 Advanced signal processing algorithms for physiological signal extraction
202(7)
8.2.1 CS and stepwise ANM
202(4)
8.2.2 SST for instantaneous vital sign detection
206(3)
8.3 Noncontact healthy status sensing
209(8)
8.3.1 Breathing disorder recognition
209(3)
8.3.2 Sleep-stage estimation
212(5)
References
217(2)
9 Radar measurement of the angular velocity of moving objects
219(26)
Eric Klinefelter
Jeffrey A. Nanzer
9.1 Radar measurements
219(2)
9.2 Interferometric measurement of angular velocity
221(3)
9.3 Measurement resolution and accuracy
224(7)
9.3.1 Resolution
224(4)
9.3.2 Accuracy
228(3)
9.4 Nonlinear distortion and mitigation
231(4)
9.5 Experimental system examples
235(6)
9.5.1 Passive 27.4-GHz correlation interferometer system
236(1)
9.5.2 Active 29.5-GHz dual interferometric-Doppler system
237(4)
9.6 Conclusions
241(1)
References
242(3)
10 Continuous-wave radar sensor for structural displacement monitoring
245(46)
Shanyue Guan
Jennifer A. Bridge
10.1 Introduction
246(1)
10.2 Background
247(7)
10.2.1 Structural health monitoring
247(1)
10.2.2 Existing displacement sensing technologies
248(1)
10.2.3 Radar techniques
248(6)
10.3 Continuous radar sensor hardware
254(8)
10.3.1 CW radar system
255(4)
10.3.2 AC-coupled radar
259(1)
10.3.3 DC-coupled radar
259(1)
10.3.4 Active transponder
260(2)
10.4 Continuous radar sensor software
262(5)
10.4.1 Signal-processing algorithms
263(4)
10.5 Continuous radar sensor measurement characterization
267(8)
10.5.1 Dynamic displacement experiments
267(3)
10.5.2 Static deflection experiments
270(3)
10.5.3 Moving load experiment
273(1)
10.5.4 Oblique angle tests
273(2)
10.6 Continuous radar full-scale structural experiments validation
275(8)
10.6.1 Sweetwater Park Bridge experiment
275(6)
10.6.2 Vehicle load experiment
281(2)
10.7 Conclusions
283(2)
References
285(6)
11 Short-distance radar sensing application
291(20)
Chen Song
Zhengxiong Li
Wenyao Xu
11.1 Introduction
291(15)
11.1.1 Smart healthcare
291(5)
11.1.2 Biometric authentication
296(10)
References
306(5)
12 Micro-Doppler signatures for sensing micro-motion
311(18)
Victor C. Chen
William J. Miceli
12.1 An introduction to micro-motion and micro-Doppler effect
311(7)
12.1.1 Micro-motion and micro-Doppler effect in radar
313(1)
12.1.2 Micro-Doppler signatures
313(5)
12.2 Angular velocity-induced micro-Doppler signatures
318(2)
12.3 Feature extraction and motion decomposition from micro-Doppler signatures
320(3)
12.3.1 Feature extraction from micro-Doppler signatures
321(1)
12.3.2 Motion decomposition from micro-Doppler signatures
322(1)
12.4 Micro-Doppler signature-based identification
323(2)
12.4.1 Micro-Doppler signature-based classification
323(1)
12.4.2 Motion identification from micro-Doppler signatures
324(1)
12.4.3 Classification, recognition, and identification using deep learning neural networks
324(1)
References
325(4)
13 Repurposing millimeter-wave communication devices for high-precision wireless sensing
329(36)
Teng Wei
Xinyu Zhang
13.1 Introduction
329(2)
13.2 mTrack: an overview
331(2)
13.3 Phase-based fine-grained mmWave tracking
333(6)
13.3.1 Basic successive tracking algorithm
333(1)
13.3.2 Tracking under background reflection
334(5)
13.4 RSS-basedAPA
339(2)
13.4.1 Locating through discrete beam steering
339(1)
13.4.2 Background RSS subtraction
340(1)
13.4.3 Opportunistic calibration
341(1)
13.5 Implementation and evaluation of mTrack
341(4)
13.5.1 Implementation
341(2)
13.5.2 Performance on a trackpad
343(1)
13.5.3 Application of mTrack
344(1)
13.6 E-Mi: an overview
345(1)
13.7 Multipath resolution framework
346(5)
13.7.1 Estimate path angles using phased arrays
347(1)
13.7.2 Virtual beamforming: match path angles
348(2)
13.7.3 Multitone ranging: estimate path length
350(1)
13.8 Dominant reflector reconstruction
351(4)
13.8.1 Locating reflecting points in environment
351(2)
13.8.2 Reconstructing dominant reflector layout and reflectivity
353(2)
13.9 Implementation and evaluation of E-Mi
355(5)
13.9.1 Implementation
355(2)
13.9.2 Effectiveness of dominant reflector reconstruction
357(3)
13.10 Summary
360(1)
References
360(5)
14 Conclusion
365(4)
Changzhan Gu
Jaime Lien
References
366(3)
Index 369
Changzhan Gu is an Associate Professor at Shanghai Jiao Tong University, China. He was a Hardware Engineer at Google, where he was involved with Project Soli at Google Advanced Technology and Projects (ATAP) and consumer hardware products at Google Hardware. He got his PhD degree from Texas Tech University, MS degree from University of Florida, and MS/BS degrees from Zhejiang University. His research focus is on microwave radar sensing technologies and their various applications.



Jaime Lien is the Lead Research Engineer of Project Soli at Google Advanced Technology and Projects (ATAP), USA. She leads a technical team developing novel radar sensing techniques and systems for human perception and interaction. She holds a Ph.D. in Electrical Engineering from Stanford University and bachelor's and Master's degrees from MIT. Her research interests include radar signal processing and sensing algorithms; modeling and analysis of the underlying RF phenomena; and inference on radar data.