| 摘要(英) |
In recent years, with the rapid development of the Internet of Things (IoT), Wi-Fi devices have become ubiquitous. Wi-Fi signals have become a popular research topic in the field of detection, recognition, and sensing. Research related to these topics increase rapidly. Under the Wi-Fi standard, Orthogonal Frequency-Division Multiplexing (OFDM), combined with Multiple-Input Multiple-Output (MIMO) systems, generates rich and complex Channel State Information (CSI). This reflects the signal′s propagation from the transmitter to the receiver in space, influenced by multipath effects such as scattering, fading, and reflection. Any activity in an indoor environment will cause changes in the CSI. The characteristics and rich variations of CSI, as well as advantages like non-contact sensing, wide sensing range, privacy protection, and low difficulty in obtaining equipment, make Wi-Fi signals the preferred choice for environmental sensing. However, the CSI itself is affected by the multipath effect, has high noise and high complexity characteristics, and it is difficult to extract useful information directly from the signal. Additionally, the OFDM system used in the Wi-Fi standard causes phase shifts in CSI. Therefore, phase correction and signal feature extraction are the key steps in applying CSI. This study, inspired by the CSI model, analyzes the impact of the OFDM system and the multipath communication model on CSI. Use different calibration equations to reduce the phase offset caused by OFDM, explores the differences in amplitude and phase performance under multipath effects in a simulated experimental environment and finally demonstrates the feasibility of CSI in practical applications. |
| 參考文獻 |
1.Wang, X., L. Gao, and S. Mao, CSI phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet of Things Journal, 2016. 3(6): p. 1113-1123.
2.Sen, S., et al. You are facing the Mona Lisa: Spot localization using PHY layer information. in Proceedings of the 10th international conference on Mobile systems, applications, and services. 2012.
3.Yang, Z., Z. Zhou, and Y. Liu, From RSSI to CSI: Indoor localization via channel response. ACM Computing Surveys (CSUR), 2013. 46(2): p. 1-32.
4.Hernandez, S.M. and E. Bulut, Wifi sensing on the edge: Signal processing techniques and challenges for real-world systems. IEEE Communications Surveys & Tutorials, 2022. 25(1): p. 46-76.
5.Xiong, J. and K. Jamieson. {ArrayTrack}: A {Fine-Grained} indoor location system. in 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13). 2013.
6.Kotaru, M., et al. Spotfi: Decimeter level localization using wifi. in Proceedings of the 2015 ACM conference on special interest group on data communication. 2015.
7.Li, X., et al. Dynamic-MUSIC: Accurate device-free indoor localization. in Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. 2016.
8.Vasisht, D., S. Kumar, and D. Katabi. {Decimeter-Level} localization with a single {WiFi} access point. in 13th USENIX symposium on networked systems design and implementation (NSDI 16). 2016.
9.Wang, Y., K. Wu, and L.M. Ni, Wifall: Device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing, 2016. 16(2): p. 581-594.
10.Wang, H., et al., RT-Fall: A real-time and contactless fall detection system with commodity WiFi devices. IEEE Transactions on Mobile Computing, 2016. 16(2): p. 511-526.
11.Xiao, J., et al. Pilot: Passive device-free indoor localization using channel state information. in 2013 IEEE 33rd International Conference on Distributed Computing Systems. 2013. IEEE.
12.Wu, C., et al., Non-invasive detection of moving and stationary human with WiFi. IEEE Journal on Selected Areas in Communications, 2015. 33(11): p. 2329-2342.
13.Qian, K., et al. PADS: Passive detection of moving targets with dynamic speed using PHY layer information. in 2014 20th IEEE international conference on parallel and distributed systems (ICPADS). 2014. IEEE.
14.Zeng, Y., P.H. Pathak, and P. Mohapatra. WiWho: WiFi-based person identification in smart spaces. in 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). 2016. IEEE.
15.Xin, T., et al. Freesense: Indoor human identification with Wi-Fi signals. in 2016 IEEE Global Communications Conference (GLOBECOM). 2016. IEEE.
16.Virmani, A. and M. Shahzad. Position and orientation agnostic gesture recognition using WiFi. in Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services. 2017.
17.Li, H., et al. WiFinger: Talk to your smart devices with finger-grained gesture. in Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2016.
18.He, W., et al. WiG: WiFi-based gesture recognition system. in 2015 24th International Conference on Computer Communication and Networks (ICCCN). 2015. IEEE.
19.Seel, T., J. Raisch, and T. Schauer, IMU-based joint angle measurement for gait analysis. Sensors, 2014. 14(4): p. 6891-6909.
20.Wang, W., A.X. Liu, and M. Shahzad. Gait recognition using wifi signals. in Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. 2016.
21.Gao, Q., et al., CSI-based device-free wireless localization and activity recognition using radio image features. IEEE Transactions on Vehicular Technology, 2017. 66(11): p. 10346-10356.
22.Zheng, X., et al. Smokey: Ubiquitous smoking detection with commercial WiFi infrastructures. in IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications. 2016. IEEE.
23.Wang, F., W. Gong, and J. Liu, On spatial diversity in WiFi-based human activity recognition: A deep learning-based approach. IEEE Internet of Things Journal, 2018. 6(2): p. 2035-2047.
24.Adib, F. and D. Katabi. See through walls with WiFi! in Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM. 2013.
25.Vakalis, S., L. Gong, and J.A. Nanzer, Imaging with wifi. IEEE Access, 2019. 7: p. 28616-28624.
26.Geng, J., D. Huang, and F. De la Torre, Densepose from wifi. arXiv preprint arXiv:2301.00250, 2022.
27.Liu, X., et al. Wi-sleep: Contactless sleep monitoring via wifi signals. in 2014 IEEE Real-Time Systems Symposium. 2014. IEEE.
28.Bao, N., et al., Wi-breath: A WiFi-based contactless and real-time respiration monitoring scheme for remote healthcare. IEEE journal of biomedical and health informatics, 2022. 27(5): p. 2276-2285.
29.Hu, J., et al. ResFi: WiFi-enabled device-free respiration detection based on deep learning. in 2022 IEEE 17th International Conference on Control & Automation (ICCA). 2022. IEEE.
30.Wang, X., C. Yang, and S. Mao. PhaseBeat: Exploiting CSI phase data for vital sign monitoring with commodity WiFi devices. in 2017 IEEE 37th international conference on distributed computing systems (ICDCS). 2017. IEEE.
31.Liu, J., et al. Tracking vital signs during sleep leveraging off-the-shelf wifi. in Proceedings of the 16th ACM international symposium on mobile ad hoc networking and computing. 2015.
32.Li, H., et al., Wi-motion: A robust human activity recognition using WiFi signals. IEEE Access, 2019. 7: p. 153287-153299.
33.Carlson, A.B., communication systems: an introduction to signal noise in electrical communication. 2002.
34.Zhang, D., et al., Calibrating phase offsets for commodity WiFi. IEEE Systems Journal, 2019. 14(1): p. 661-664.
35.Jiang, Z., et al., Eliminating the barriers: Demystifying wi-fi baseband design and introducing the picoscenes wi-fi sensing platform. IEEE Internet of Things Journal, 2021. 9(6): p. 4476-4496. |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 (2029-7-1以後開放)
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit