Kirsch, 2014, Precise local-positioning for autonomous situation awareness in the Internet of Things
Zhong, 2018, Internet of mission-critical things: human and animal classification-a device-free sensing approach, IEEE Int. Things J., 5, 3369, 10.1109/JIOT.2017.2760322
A.A. Adebomehin, S.D. Walker, Impulse radio ultrawideband D2D-based localization for ultra-dense 5G networks, in: 2017 IEEE 18th Wireless and Microwave Technology Conference, WAMICON 2017.
Pinky, 2019, Smart device localization using femtocell and macro base station based path loss models in IoT networks, 1
Wang, 2019, Comparative analysis of channel models for industrial IoT wireless communication, IEEE Access, 7, 91627, 10.1109/ACCESS.2019.2927217
Koudouridis, 2018, A method for the generation of radio signal coverage maps for dense networks, 1
D. Dardari, N. Decarli, A. Guerra, F. Guidi, The future of ultra-wideband localization in RFID, in: 2016 IEEE International Conference on RFID, RFID 2016.
Dardari, 2009, Ranging with ultrawide bandwidth signals in multipath environments, Proc. IEEE, 97, 404, 10.1109/JPROC.2008.2008846
Bosneag, 2018, Intelligent network management mechanisms as a step towards SG, 52
Fadlullah, 2017, State-of-the-art deep learning: evolving machine intelligence toward tomorrow's intelligent network traffic control systems, IEEE Commun. Surv. Tutor., 19, 2432, 10.1109/COMST.2017.2707140
Mao, 2018, Deep learning for intelligent wireless networks: a comprehensive survey, IEEE Commun. Surv. Tutor., 20, 2595, 10.1109/COMST.2018.2846401
Lyu, 2018, Intelligent context-aware communication paradigm design for IoVs based on data analytics, IEEE Netw., 32, 74, 10.1109/MNET.2018.1800067
Lyu, 2018, DBCC: leveraging link perception for distributed beacon congestion control in VANETs, IEEE Int. Things J., 5, 4237, 10.1109/JIOT.2018.2844826
Guermah, 2019, A robust GNSS LOS/multipath signal classifier based on the fusion of information and machine learning for intelligent transportation systems, 94
Hsu, 2018, GNSS multipath detection using a machine learning approach, 1
Chitambira, 2017, NLOS identification and mitigation for geolocation using least-squares support vector machines, 1
Nguyen, 2018, NLOS identification in WLANs using deep LSTM with CNN features, Sensors (Switzerland), 18, 1, 10.3390/s18114057
Fan, 2019, Non-line-of-sight identification based on unsupervised machine learning in ultra wideband systems, IEEE Access, 7, 32464, 10.1109/ACCESS.2019.2903236
Ding, 2014, Efficient indoor fingerprinting localization technique using regional propagation model, IEICE Trans. Commun., E97-B, 1728, 10.1587/transcom.E97.B.1728
X. Cai, X. Li, R. Yuan, Y. Hei, Identification and mitigation of NLOS based on channel state information for indoor WiFi localization, in: 2015 International Conference on Wireless Communications and Signal Processing, WCSP 2015, pp. 1–5.
Li, 2017, NLOS identification and mitigation based on channel state information for indoor WiFi localisation, IET Commun., 11, 531, 10.1049/iet-com.2016.0562
Xiao, 2015, Non-line-of-sight identification and mitigation using received signal strength, IEEE Trans. Wirel. Commun., 14, 1689, 10.1109/TWC.2014.2372341
Z. Zeng, S. Liu, L. Wang, UWB NLOS identification with feature combination selection based on genetic algorithm, in: 2019 IEEE International Conference on Consumer Electronics, ICCE 2019, pp. 1–5.
Kristensen, 2019, Non-line-of-sight identification for UWB indoor positioning systems using support vector machines, 1
Maranò, 2010, NLOS identification and mitigation for localization based on UWB experimental data, IEEE J. Sel. Areas Commun., 28, 1026, 10.1109/JSAC.2010.100907
Bregar, 2018, Improving indoor localization using convolutional neural networks on computationally restricted devices, IEEE Access, 6, 17429, 10.1109/ACCESS.2018.2817800
Kolakowski, 2018, Detection of direct path component absence in NLOS UWB channel, 247
R. Zandian, U. Witkowski, Differential NLOS error detection in UWB-based localization systems using logistic regression, in: 2018 15th Workshop on Positioning, Navigation and Communications, WPNC 2018, pp. 1–6.
Krishnan, 2018, Improving UWB based indoor positioning in industrial environments through machine learning, 1484
Choi, 2018, Deep learning based NLOS identification with commodity WLAN devices, IEEE Trans. Veh. Technol., 67, 3295, 10.1109/TVT.2017.2780121
Ramadan, 2018, NLOS identification for indoor localization using random forest algorithm, 1
DecaWave, DWM1000 User Manual, v.2.09 (2016).
Cwalina, 2018, An off-body narrowband and ultra-wide band channel model for body area networks in a ferryboat environment, Appl. Sci. (Switzerland), 8, 1
Viola, 2001, Rapid object detection using a boosted cascade of simple features
Freund, 2015, A short introduction to Boosting, 14, 771
Cwalina, 2019, Deep learning-based LOS and NLOS identification in wireless body area networks, MDPI Sens., 19, 4229, 10.3390/s19194229
Goodfellow, 2016
Patterson, 2017
Kingma, 2014, 1