Device-Free Localization Techniques: A Review

Abd Al-Rahman Tariq Rasheed; Aseel Hameed AL-Nakkash; Osama Abbas Al Tameemi; Debashri Roy

doi:10.51173/jt.v5i4.1844

Authors

Abd Al-Rahman Tariq Rasheed Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Aseel Hameed AL-Nakkash Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Osama Abbas Al Tameemi Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.
Debashri Roy Department of Computer Science and Engineering, The University of Texas Arlington, Arlington, USA

DOI:

https://doi.org/10.51173/jt.v5i4.1844

Keywords:

Indoor Localization, Indoor Tracking, Device-Free, Radio Tomography, Fingerprinting, Passive-Localization

Abstract

Device-free localization (DFL) has emerged as a transformative technology for tracking objects and individuals without requiring them to carry electronic devices. This paper reviews two pivotal techniques of DFL: Link Quality Measurement and Link Scattering techniques. Link Quality Measurement focuses on evaluating the quality of wireless links through metrics like Received-Signal-Strength and Channel-State-Information, offering simplicity and reliability. Meanwhile, Link Scattering harnesses signal reflections and diffractions caused by environmental obstacles to estimate device-free target positions. This review provides insights of Link Quality Measurement, Link Scattering methods and highlighting DFL metrics. By shedding light on these critical aspects, it offers valuable insights into the current state of DFL technology and its potential in diverse domains, ranging from smart environments to security systems.

Downloads

Download data is not yet available.

Author Biographies

Abd Al-Rahman Tariq Rasheed, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Computer Techniques Engineering

Aseel Hameed AL-Nakkash, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Computer Techniques Engineering

Osama Abbas Al Tameemi, Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Computer Techniques Engineering

Debashri Roy, Department of Computer Science and Engineering, The University of Texas Arlington, Arlington, USA

References

H. Wang, D. Zhang, Y. Wang, J. Ma, Y. Wang, and S. Li, “RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices,” IEEE Trans Mob Comput, vol. 16, no. 2, pp. 511–526, 2017, doi: 10.1109/TMC.2016.2557795.

X. Zheng, J. Wang, L. Shangguan, Z. Zhou, and Y. Liu, “Smokey: Ubiquitous smoking detection with commercial WiFi infrastructures,” in IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, 2016, pp. 1–9. doi: 10.1109/INFOCOM.2016.7524399.

D. Zhang et al., “Fine-grained localization for multiple transceiver-free objects by using RF-based technologies,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 6, pp. 1464–1475, 2013.

Y. Zeng, P. H. Pathak, and P. Mohapatra, “Analyzing shopper’s behavior through WiFi signals,” in WPA 2015 - Proceedings of the 2nd Workshop on Physical Analytics, 2015. doi: 10.1145/2753497.2753508.

M. Seifeldin, A. Saeed, A. E. Kosba, A. El-Keyi, and M. Youssef, “Nuzzer: A large-scale device-free passive localization system for wireless environments,” IEEE Trans Mob Comput, vol. 12, no. 7, 2013, doi: 10.1109/TMC.2012.106.

I. Sabek, M. Youssef, and A. V. Vasilakos, “ACE: An accurate and efficient multi-entity device-free WLAN localization system,” IEEE Trans Mob Comput, vol. 14, no. 2, 2015, doi: 10.1109/TMC.2014.2320265.

J. Wang et al., “LiFS: Low human-effort, device-free localization with fine-grained subcarrier information,” in Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2016. doi: 10.1145/2973750.2973776.

K. Sugino, S. Katayama, Y. Niwa, S. Shiramatsu, T. Ozono, and T. Shintani, “A Bluetooth-Based Device-Free Motion Detector for a Remote Elder Care Support System,” in Proceedings - 2015 IIAI 4th International Congress on Advanced Applied Informatics, IIAI-AAI 2015, 2016. doi: 10.1109/IIAI-AAI.2015.229.

M. Bocca, O. Kaltiokallio, N. Patwari, and S. Venkatasubramanian, “Multiple target tracking with rf sensor networks,” IEEE Trans Mob Comput, vol. 13, no. 8, 2014, doi: 10.1109/TMC.2013.92.

C. Xu et al., “SCPL: Indoor device-free multi-subject counting and localization using radio signal strength,” in IPSN 2013 - Proceedings of the 12th International Conference on Information Processing in Sensor Networks, Part of CPSWeek 2013, 2013. doi: 10.1145/2461381.2461394.

D. Zhang, Y. Liu, X. Guo, and L. M. Ni, “RASS: A real-time, accurate, and scalable system for tracking transceiver-free objects,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 5, 2013, doi: 10.1109/TPDS.2012.134.

B. Gulmezoglu, M. B. Guldogan, and S. Gezici, “Multiperson Tracking With a Network of Ultrawideband Radar Sensors Based on Gaussian Mixture PHD Filters,” IEEE Sens J, vol. 15, no. 4, pp. 2227–2237, 2015, doi: 10.1109/JSEN.2014.2372312.

Y. Kilic, H. Wymeersch, A. Meijerink, M. J. Bentum, and W. G. Scanlon, “Device-free person detection and ranging in UWB networks,” IEEE Journal on Selected Topics in Signal Processing, vol. 8, no. 1, 2014, doi: 10.1109/JSTSP.2013.2281780.

B. Yang, Q. Wei, and M. Zhang, “Multiple human location in a distributed binary pyroelectric infrared sensor network,” Infrared Phys Technol, vol. 85, 2017, doi: 10.1016/j.infrared.2017.06.007.

Q. Hao, F. Hu, and Y. Xiao, “Multiple human tracking and identification with wireless distributed pyroelectric sensor systems,” IEEE Syst J, vol. 3, no. 4, 2009, doi: 10.1109/JSYST.2009.2035734.

S. Tao, M. Kudo, B. N. Pei, H. Nonaka, and J. Toyama, “Multiperson Locating and Their Soft Tracking in a Binary Infrared Sensor Network,” IEEE Trans Hum Mach Syst, vol. 45, no. 5, 2015, doi: 10.1109/THMS.2014.2365466.

D. Zhang, J. Zhou, M. Guo, J. Cao, and T. Li, “TASA: Tag-free activity sensing using RFID tag arrays,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 4, 2011, doi: 10.1109/TPDS.2010.118.

J. Wang, J. Xiong, H. Jiang, X. Chen, and D. Fang, “D-Watch: Embracing ‘bad’ Multipaths for Device-Free Localization with COTS RFID Devices,” IEEE/ACM Transactions on Networking, vol. 25, no. 6, 2017, doi: 10.1109/TNET.2017.2747583.

M. Moussa and M. Youssef, “Smart devices for smart environments: Device-free passive detection in real environments,” in 7th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2009, 2009. doi: 10.1109/PERCOM.2009.4912826.

N. Ahmed, S. Kamel Gharghan, A. H. Mutlag, and M. G. M. Abdolrasol, “Children Tracking System Based on ZigBee Wireless Network and Neural Network,” Journal of Techniques, vol. 5, no. 1, pp. 103–113, 2023, doi: 10.51173/jt.v5i1.838.

T. Xin, B. Guo, Z. Wang, M. Li, Z. Yu, and X. Zhou, “FreeSense: Indoor human identification with wi-fi signals,” in 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings, 2016. doi: 10.1109/GLOCOM.2016.7841847.

Y. Zeng, 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 - Proceedings, 2016. doi: 10.1109/IPSN.2016.7460727.

J. Zhang, B. Wei, W. Hu, and S. S. Kanhere, “WiFi-ID: Human identification using WiFi signal,” in Proceedings - 12th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2016, 2016. doi: 10.1109/DCOSS.2016.30.

J. Xiao, K. Wu, Y. Yi, L. Wang, and L. M. Ni, “Pilot: Passive device-free indoor localization using channel state information,” in Proceedings - International Conference on Distributed Computing Systems, 2013. doi: 10.1109/ICDCS.2013.49.

K. Hong, T. Wang, J. Liu, Y. Wang, and Y. Shen, “A Learning-Based AoA Estimation Method for Device-Free Localization,” IEEE Communications Letters, vol. 26, no. 6, 2022, doi: 10.1109/LCOMM.2022.3158837.

T. Xing, D. Fang, X. Chen, M. Jin, X. Zheng, and K. Zhao, “Doppler effect based moving target detection adaptive to speed,” in CSAR 2015 - Proceedings of the 1st ACM Workshop on Context Sensing and Activity Recognition, co-located with SenSys 2015, 2015. doi: 10.1145/2820716.2820722.

X. Quan, J. W. Choi, and S. H. Cho, “In-bound/Out-bound detection of people’s movements using an IR-UWB radar system,” in 13th International Conference on Electronics, Information, and Communication, ICEIC 2014 - Proceedings, 2014. doi: 10.1109/ELINFOCOM.2014.6914407.

M. F. Mosleh, F. A. Abed, and Z. Abbas, “Improving Indoor Localization System Using a Partitioning Technique Based on RSS and ToA,” Journal of Techniques, vol. 3, no. 1, pp. 47–54, 2021, Accessed: Sep. 27, 2023. [Online]. Available: http://journal.mtu.edu.iq

R. Zhou, X. Lu, P. Zhao, and J. Chen, “Device-Free Presence Detection and Localization with SVM and CSI Fingerprinting,” in IEEE Sensors Journal, 2017. doi: 10.1109/JSEN.2017.2762428.

R. Zhou, M. Hao, X. Lu, M. Tang, and Y. Fu, “Device-free localization based on CSI fingerprints and deep neural networks,” in 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2018, 2018. doi: 10.1109/SAHCN.2018.8397121.

X. Luo, B. Shen, X. Guo, G. Luo, and G. Wang, “Human tracking using ceiling pyroelectric infrared sensors,” in 2009 IEEE International Conference on Control and Automation, ICCA 2009, 2009. doi: 10.1109/ICCA.2009.5410239.

J. Wilson and N. Patwari, “See-through walls: Motion tracking using variance-based radio tomography networks,” IEEE Trans Mob Comput, vol. 10, no. 5, 2011, doi: 10.1109/TMC.2010.175.

Q. Hao, D. J. Brady, B. D. Guenther, J. B. Burchett, M. Shankar, and S. Feller, “Human tracking with wireless distributed pyroelectric sensors,” IEEE Sens J, vol. 6, no. 6, 2006, doi: 10.1109/JSEN.2006.884562.

D. Lieckfeldt, J. You, and D. Timmermann, “Passive tracking of transceiver-free users with RFID,” in Communications in Computer and Information Science, 2009. doi: 10.1007/978-3-642-10263-9_28.

Y. Guo, K. Huang, N. Jiang, X. Guo, Y. Li, and G. Wang, “An exponential-rayleigh model for RSS-based device-free localization and tracking,” IEEE Trans Mob Comput, vol. 14, no. 3, 2015, doi: 10.1109/TMC.2014.2329007.

J. Wilson and N. Patwari, “A Fade-level skew-laplace signal strength model for device-free localization with wireless networks,” IEEE Trans Mob Comput, vol. 11, no. 6, 2012, doi: 10.1109/TMC.2011.102.

Z. Wang, H. Liu, S. Xu, X. Bu, and J. An, “Bayesian device-free localization and tracking in a binary rf sensor network,” Sensors (Switzerland), vol. 17, no. 5, 2017, doi: 10.3390/s17050969.

C. Han, Q. Tan, L. Sun, H. Zhu, and J. Guo, “CSI frequency domain fingerprint-based passive indoor human detection,” Information (Switzerland), vol. 9, no. 4, 2018, doi: 10.3390/info9040095.

D. Zhang and L. M. Ni, “Dynamic clustering for tracking multiple transceiver-free objects,” in 7th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2009, 2009. doi: 10.1109/PERCOM.2009.4912777.

L. Song and Y. Wang, “Multiple target counting and tracking using binary proximity sensors: Bounds, coloring, and filter,” in Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2014. doi: 10.1145/2632951.2632959.

T. Li, Y. Wang, L. Song, and H. Tan, “On target counting by sequential snapshots of binary proximity sensors,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015. doi: 10.1007/978-3-319-15582-1_2.

O. Kaltiokallio, R. Hostettler, and N. Patwari, “A Novel Bayesian Filter for RSS-Based Device-Free Localization and Tracking,” IEEE Trans Mob Comput, vol. 20, no. 3, 2021, doi: 10.1109/TMC.2019.2953474.

S. Xu, H. Liu, and F. Gao, “An Enhanced Radio Tomographic Imaging with CSI-MIMO Measurements,” in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, 2018. doi: 10.1109/PIMRC.2018.8580720.

W. Ruan, L. Yao, Q. Z. Sheng, N. J. G. Falkner, and X. Li, “TagTrack: Device-free localization and tracking using passive RFID tags,” in MobiQuitous 2014 - 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2014. doi: 10.4108/icst.mobiquitous.2014.258004.

R. C. Shit et al., “Ubiquitous Localization (UbiLoc): A Survey and Taxonomy on Device Free Localization for Smart World,” IEEE Communications Surveys and Tutorials, vol. 21, no. 4, pp. 3532–3564, Oct. 2019, doi: 10.1109/COMST.2019.2915923.

J. Wilson and N. Patwari, “Radio tomographic imaging with wireless networks,” IEEE Trans Mob Comput, vol. 9, no. 5, 2010, doi: 10.1109/TMC.2009.174.

O. Kaltiokallio, M. Bocca, and N. Patwari, “Enhancing the accuracy of radio tomographic imaging using channel diversity,” in MASS 2012 - 9th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, 2012. doi: 10.1109/MASS.2012.6502524.

M. Khaledi, S. K. Kasera, N. Patwari, and M. Bocca, “Energy efficient radio tomographic imaging,” in 2014 11th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2014, 2014. doi: 10.1109/SAHCN.2014.6990401.

O. and P. N. Bocca Maurizio and Kaltiokallio, “Radio Tomographic Imaging for Ambient Assisted Living,” in Evaluating AAL Systems Through Competitive Benchmarking, S. Chessa Stefano and Knauth, Ed., Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 108–130.

M. Speekenbrink, “A tutorial on particle filters,” J Math Psychol, vol. 73, 2016, doi: 10.1016/j.jmp.2016.05.006.

R. R. Yager, “Intelligent control of the hierarchical agglomerative clustering process,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 30, no. 6, 2000, doi: 10.1109/3477.891145.

P. Closas and M. F. Bugallo, “Improving accuracy by iterated multiple particle filtering,” IEEE Signal Process Lett, vol. 19, no. 8, 2012, doi: 10.1109/LSP.2012.2205565.

B. Wei, A. Varshney, N. Patwari, W. Hu, T. Voigt, and C. T. Chou, “dRTI: Directional radio tomographic imaging,” in IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week), 2015. doi: 10.1145/2737095.2737118.

J. Wang, D. Fang, X. Chen, Z. Yang, T. Xing, and L. Cai, “LCS: Compressive sensing based device-free localization for multiple targets in sensor networks,” in Proceedings - IEEE INFOCOM, 2013. doi: 10.1109/INFCOM.2013.6566752.

L. Chang et al., “FitLoc: Fine-grained and low-cost device-free localization for multiple targets over various areas,” IEEE/ACM Transactions on Networking, vol. 25, no. 4, 2017, doi: 10.1109/TNET.2017.2669339.

Y. Zheng and A. Men, “Through-wall tracking with radio tomography networks using foreground detection,” in IEEE Wireless Communications and Networking Conference, WCNC, 2012. doi: 10.1109/WCNC.2012.6214374.

M. McCracken, M. Bocca, and N. Patwari, “Joint ultra-wideband and signal strength-based through-building tracking for tactical operations,” in 2013 IEEE International Conference on Sensing, Communications and Networking, SECON 2013, 2013. doi: 10.1109/SAHCN.2013.6645000.

Y. Zhao and N. Patwari, “Histogram distance-based radio tomographic localization,” in IPSN’12 - Proceedings of the 11th International Conference on Information Processing in Sensor Networks, 2012. doi: 10.1145/2185677.2185712.

C. P. Wang and B. Jo, “Applications of a Kullback-Leibler divergence for comparing non-nested models,” Stat Modelling, vol. 13, no. 5–6, 2013, doi: 10.1177/1471082X13494610.

Y. Zhao, N. Patwari, J. M. Phillips, and S. Venkatasubramanian, “Radio tomographic imaging and tracking of stationary and moving people via kernel distance,” in IPSN 2013 - Proceedings of the 12th International Conference on Information Processing in Sensor Networks, Part of CPSWeek 2013, 2013. doi: 10.1145/2461381.2461410.

Y. Zhao and N. Patwari, “Noise reduction for variance-based device-free localization and tracking,” in 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2011, 2011. doi: 10.1109/SAHCN.2011.5984895.

O. Kaltiokallio, H. Yigitler, and R. Jantti, “A three-state received signal strength model for device-free localization,” IEEE Trans Veh Technol, vol. 66, no. 10, 2017, doi: 10.1109/TVT.2017.2701399.

Q. Lei, H. Zhang, H. Sun, and L. Tang, “Fingerprint-Based Device-Free Localization in Changing Environments Using Enhanced Channel Selection and Logistic Regression,” IEEE Access, vol. 6, 2017, doi: 10.1109/ACCESS.2017.2784387.

B. Mager, P. Lundrigan, and N. Patwari, “Fingerprint-based device-free localization performance in changing environments,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 11, 2015, doi: 10.1109/JSAC.2015.2430515.

B. Mager, “Maintaining accuracy of device-free localization systems in changing environments,” 2014.

S. Kianoush, S. Savazzi, F. Vicentini, V. Rampa, and M. Giussani, “Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces,” IEEE Internet Things J, vol. 4, no. 2, 2017, doi: 10.1109/JIOT.2016.2624800.

J. Zhang, W. Xiao, S. Zhang, and S. Huang, “Device-free localization via an extreme learning machine with parameterized geometrical feature extraction,” Sensors (Switzerland), vol. 17, no. 4, 2017, doi: 10.3390/s17040879.

G. Bin Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1–3, 2006, doi: 10.1016/j.neucom.2005.12.126.

Y. Y. Chiang, W. H. Hsu, S. C. Yeh, Y. C. Li, and J. S. Wu, “Fuzzy support vector machines for device-free localization,” in 2012 IEEE I2MTC - International Instrumentation and Measurement Technology Conference, Proceedings, 2012. doi: 10.1109/I2MTC.2012.6229338.

G. Huang, G.-B. Huang, S. Song, and K. You, “Trends in extreme learning machines: A review,” Neural Networks, vol. 61, pp. 32–48, 2015, doi: https://doi.org/10.1016/j.neunet.2014.10.001.

G. Bin Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 2, 2012, doi: 10.1109/TSMCB.2011.2168604.

W. Xiao, J. Zhang, Y. Li, S. Zhang, and W. Yang, “Class-specific cost regulation extreme learning machine for imbalanced classification,” Neurocomputing, vol. 261, 2017, doi: 10.1016/j.neucom.2016.09.120.

W. Zong and G. Bin Huang, “Face recognition based on extreme learning machine,” Neurocomputing, vol. 74, no. 16, 2011, doi: 10.1016/j.neucom.2010.12.041.

A. A. Mohammed, R. Minhas, Q. M. Jonathan Wu, and M. A. Sid-Ahmed, “Human face recognition based on multidimensional PCA and extreme learning machine,” Pattern Recognit, vol. 44, no. 10–11, 2011, doi: 10.1016/j.patcog.2011.03.013.

H. Zhang, Y. Yin, and S. Zhang, “An improved ELM algorithm for the measurement of hot metal temperature in blast furnace,” Neurocomputing, vol. 174, 2016, doi: 10.1016/j.neucom.2015.04.106.

S. Zhang, X. Chen, and Y. Yin, “An ELM Based Online Soft Sensing Approach for Alumina Concentration Detection,” Math Probl Eng, vol. 2015, 2015, doi: 10.1155/2015/268132.

W. Xiao and Y. Lu, “Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine,” Math Probl Eng, vol. 2015, 2015, doi: 10.1155/2015/790412.

R. Minhas, A. Baradarani, S. Seifzadeh, and Q. M. Jonathan Wu, “Human action recognition using extreme learning machine based on visual vocabularies,” Neurocomputing, vol. 73, no. 10–12, 2010, doi: 10.1016/j.neucom.2010.01.020.

J. Cao et al., “Landmark recognition with sparse representation classification and extreme learning machine,” J Franklin Inst, vol. 352, no. 10, 2015, doi: 10.1016/j.jfranklin.2015.07.002.

J. Cao, T. Chen, and J. Fan, “Landmark recognition with compact BoW histogram and ensemble ELM,” Multimed Tools Appl, vol. 75, no. 5, 2016, doi: 10.1007/s11042-014-2424-1.

C. Pan, D. S. Park, Y. Yang, and H. M. Yoo, “Leukocyte image segmentation by visual attention and extreme learning machine,” Neural Comput Appl, vol. 21, no. 6, 2012, doi: 10.1007/s00521-011-0522-9.

M. Youssef, M. Mah, and A. Agrawala, “Challenges: Device-free passive localization for wireless environments,” in Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2007. doi: 10.1145/1287853.1287880.

Z. Zhou, Z. Yang, C. Wu, L. Shangguan, and Y. Liu, “Towards omnidirectional passive human detection,” in Proceedings - IEEE Infocom, 2013. doi: 10.1109/INFCOM.2013.6567118.

Z. Zhou, Z. Yang, C. Wu, Y. Liu, and L. M. Ni, “On Multipath Link Characterization and Adaptation for Device-Free Human Detection,” in Proceedings - International Conference on Distributed Computing Systems, 2015. doi: 10.1109/ICDCS.2015.47.

C. Wu, Z. Yang, Z. Zhou, X. Liu, Y. Liu, and J. Cao, “Non-invasive detection of moving and stationary human with WiFi,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 11, 2015, doi: 10.1109/JSAC.2015.2430294.

F. Adib and D. Katabi, “See through walls with WiFi!,” in Computer Communication Review, 2013. doi: 10.1145/2534169.2486039.

K. Joshi, D. Bharadia, M. Kotaru, and S. Katti, “WiDeo: Fine-grained device-free motion tracing using RF backscatter,” in Proceedings of the 12th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2015, 2015.

S. C. Carey and W. R. Scott, “Software defined radio for stepped-frequency, ground-penetrating radar,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2017. doi: 10.1109/IGARSS.2017.8128082.

L. Yang, Q. Lin, X. Li, T. Liu, and Y. Liu, “See through walls with COTS RFID system!,” in Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2015. doi: 10.1145/2789168.2790100.

“A Geometric Approach to Device-Free Motion Localization Using Signal Strength - tr5b48ba663d95d - Rutgers University.” Accessed: Jul. 22, 2023. [Online]. Available: https://scholarship.libraries.rutgers.edu/esploro/outputs/technicalDocumentation/991031550038004646/filesAndLinks?index=0

S. Shi, S. Sigg, and Y. Ji, “Passive detection of situations from ambient FM-radio signals,” in UbiComp’12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 2012. doi: 10.1145/2370216.2370440.

T. Wei and X. Zhang, “MTrack: High-precision passive tracking using millimeter wave radios,” in Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2015. doi: 10.1145/2789168.2790113.

K. Ngamakeur, S. Yongchareon, J. Yu, and S. U. Rehman, “A Survey on Device-Free Indoor Localization and Tracking in the Multi-Resident Environment,” ACM Comput. Surv., vol. 53, no. 4, Jul. 2020, doi: 10.1145/3396302.

Q. Pu, S. Gupta, S. Gollakota, and S. Patel, “Whole-home gesture recognition using wireless signals,” in Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2013. doi: 10.1145/2500423.2500436.

S. Palipana, P. Agrawal, and D. Pesch, “Channel state information based human presence detection using non-linear techniques,” in Proceedings of the 3rd ACM Conference on Systems for Energy-Efficient Built Environments, BuildSys 2016, 2016. doi: 10.1145/2993422.2993579.

W. Wang, A. X. Liu, M. Shahzad, K. Ling, and S. Lu, “Understanding and modeling of WiFi signal based human activity recognition,” in Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2015. doi: 10.1145/2789168.2790093.

A. Luong, T. Schmid, and N. Patwari, “Demo Abstract: A platform enabling local oscillator frequency synchronization,” in Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016, 2016. doi: 10.1145/2994551.2996532.

N. D. Lane and P. Georgiev, “Can deep learning revolutionize mobile sensing?,” in HotMobile 2015 - 16th International Workshop on Mobile Computing Systems and Applications, 2015. doi: 10.1145/2699343.2699349.

X. Li, Y. Zhang, I. Marsic, A. Sarcevic, and R. S. Burd, “Deep learning for RFID-based activity recognition,” in Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016, 2016. doi: 10.1145/2994551.2994569.

G. Wang, J. M. Munoz-Ferreras, C. Gu, C. Li, and R. Gomez-Garcia, “Application of linear-frequency-modulated continuous-wave (LFMCW) radars for tracking of vital signs,” IEEE Trans Microw Theory Tech, vol. 62, no. 6, 2014, doi: 10.1109/TMTT.2014.2320464.

R. Ravichandran, E. Saba, K. Y. Chen, M. Goel, S. Gupta, and S. N. Patel, “WiBreathe: Estimating respiration rate using wireless signals in natural settings in the home,” in 2015 IEEE International Conference on Pervasive Computing and Communications, PerCom 2015, 2015. doi: 10.1109/PERCOM.2015.7146519.

F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller, “Smart homes that monitor breathing and heart rate,” in Conference on Human Factors in Computing Systems - Proceedings, 2015. doi: 10.1145/2702123.2702200.