Abstract
Passive human detection and localization is the basis for a broad range of intelligent scenarios including unmanned supermarket, health monitoring, etc. Existing computer vision or wearable sensor based methods though can obtain high precision, they still face some inherent defects, such as privacy issues, battery power limitations. Based on the human movement induced backscattered signal changes, we propose a device-free human detection and localization system on radio-frequency identification (RFID) devices. The system extracts environment-independent features from both RSSI and phase for dynamic monitoring in the first stage, then the target is further located if the moving human is detected. In particular, an overcomplete dictionary is learned when creating the fingerprint library, which helps to make the representation of the location more compact and computationally simple. Moreover, PCA based dimensionality reduction method is then adopted to acquire valid features to determine the final position. Extensive experiments conducted in real-life office and bedroom demonstrate that the proposed system provides high accuracy for human detection and achieves the average distance error of less than 1 m.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Ali-Loytty, S., Perala, T., Honkavirta, V., Piche, R.: Fingerprint Kalman filter in indoor positioning applications. In: 2009 IEEE Control Applications, (CCA) Intelligent Control, (ISIC), pp. 1678–1683 (2009)
Arshad, S., et al.: Wi-chase: a WiFi based human activity recognition system for sensorless environments. In: IEEE International Symposium on A World of Wireless (2017)
Bu, Y., et al.: RF-dial: an RFID-based 2D human-computer interaction via tag array. In: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, pp. 837–845, April 2018
Castillo-Cara, M., Lovón-Melgarejo, J., Bravo-Rocca, G., Orozco-Barbosa, L., García-Varea, I.: An empirical study of the transmission power setting for bluetooth-based indoor localization mechanisms. Sensors 17(6), 1318 (2017)
Dan, W., Zhang, D., Xu, C., Wang, Y., Hao, W.: WiDir: walking direction estimation using wireless signals. In: ACM International Joint Conference on Pervasive & Ubiquitous Computing (2016)
Gao, Q., et al.: CSI-based device-free wireless localization and activity recognition using radio image features. IEEE Trans. Veh. Technol. 66(11), 10346–10356 (2017)
Georgievska, S., et al.: Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach. J. Big Data 6(1), 31 (2019)
Gong, Y., Xie, L., Wang, C., Bu, Y., Lu, S.: RF-brush: 3D human-computer interaction via linear tag array. In: 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) (2018)
Itoh, K.: Analysis of the phase unwrapping algorithm. Appl. Opt. 21(14), 2470 (1982)
Lau, S.L., König, I., David, K., Parandian, B., Carius-Düssel, C., Schultz, M.: Supporting patient monitoring using activity recognition with a smartphone. In: International Symposium on Wireless Communication Systems (2010)
Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, pp. 40–44 (2009)
Peng, Y., Fan, W., Xin, D., Xing, Z.: An iterative weighted KNN (IW-KNN) based indoor localization method in bluetooth low energy (BLE) environment. In: Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People, & Smart World Congress (2017)
Qian, K., Wu, C., Zhang, Y., Zhang, G., Yang, Z., Liu, Y.: Widar2.0: passive human tracking with a single Wi-Fi link. In: Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2018, pp. 350–361 (2018)
Qian, K., Wu, C., Zheng, Y., Liu, Y., Zhou, Z.: PADS: passive detection of moving targets with dynamic speed using phy layer information. In: IEEE International Conference on Parallel & Distributed Systems (2015)
Sample, A.P., Macomber, C., Jiang, L.T., Smith, J.R.: Optical localization of passive uhf RFID tags with integrated leds. In: IEEE International Conference on RFID (2016)
Wang, C., et al.: Multi - touch in the air: device-free finger tracking and gesture recognition via cots RFID. In: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, pp. 1691–1699, April 2018
Wang, C., Liu, J., Chen, Y., Xie, L., Liu, H.B., Lu, S.: RF-kinect: a wearable RFID-based approach towards 3D body movement tracking. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2(1), 41:1–41:28 (2018)
Wang, C., Xie, L., Wang, W., Chen, Y., Bu, Y., Lu, S.: RF-ECG: heart rate variability assessment based on cots RFID tag array. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 2(2), 85:1–85:26 (2018)
Wang, J., Vasisht, D., Katabi, D.: RF-IDRAW: virtual touch screen in the air using RF signals. In: ACM Conference on Sigcomm (2014)
Wu, C., Zheng, Y., Xiao, C.: Automatic radio map adaptation for indoor localization using smartphones. IEEE Trans. Mob. Comput. PP(99), 1 (2018)
Xi, W., et al.: Electronic frog eye: counting crowd using WiFi. In: IEEE Conference on Computer Communications, IEEE INFOCOM 2014, pp. 361–369, April 2014
Xu, H., Caramanis, C., Sanghavi, S.: Robust PCA via outlier pursuit. IEEE Trans. Inf. Theory 58(5), 3047–3064 (2012)
Yang, Z., Zhou, Z., Liu, Y.: From RSSI to CSI: indoor localization via channel response. ACM Comput. Surv. 46(2), 25:1–25:32 (2013)
Yu, G., Zhan, J., Ji, Y., Jie, L., Gao, S.: MoSense: a RF-based motion detection system via off-the-shelf wifi devices. IEEE Internet of Things J. PP(99), 1 (2017)
Yu, J., Na, Z., Liu, X., Deng, Z.: WiFi/PDR-integrated indoor localizationusing unconstrained smartphones. EURASIP J. Wirel. Commun. Network. 2019(1), 41 (2019)
Zhang, S., Mccullagh, P., Nugent, C., Zheng, H.: Activity monitoring using a smart phone’s accelerometer with hierarchical classification. In: Sixth International Conference on Intelligent Environments (2010)
Zhongqin, W., Min, X., Ning, Y., Ruchuan, W., Haiping, H.: Computer vision-assisted region-of-interest rfid tag recognition and localization in multipath-prevalent environments. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 3(29), 1–30 (2019)
Zhu, D., Zhao, B., Wang, S.: Mobile target indoor tracking based on multi-direction weight position Kalman filter. Comput. Netw. 141, 115–127 (2018)
Zhu, S., Wang, S., Zhang, F., Zhang, Y., Feng, Y., Huang, W.: Environmentally adaptive real-time detection of RFID false readings in a new practical scenario. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 338–345, October 2018
Acknowledgement
This work was supported by the National Natural Science Foundation of China (61601459).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, W., Zhu, S., Wang, S., Xie, J., Zhang, Y. (2020). Sparse Representation for Device-Free Human Detection and Localization with COTS RFID. In: Wen, S., Zomaya, A., Yang, L. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11944. Springer, Cham. https://doi.org/10.1007/978-3-030-38991-8_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-38991-8_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38990-1
Online ISBN: 978-3-030-38991-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)