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Eng., vol.1149, no.1, p.012007, 2021. 10.1088\/1757-899x\/1149\/1\/012007","DOI":"10.1088\/1757-899X\/1149\/1\/012007"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] E.B. Abubakirov, R.M. Rozental', and V.P. Tarakanov, \u201cComparison of Efficiencies of External Signal Supply Circuits in a Relativistic Backward-Wave Tube with Resonant Reflector,\u201d J. Commun. Technol. Electron. vol.64, no.1, pp.59-63, 2019. 10.1134\/s1064226919010017","DOI":"10.1134\/S1064226919010017"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] G. Hushe and R. Suryawanshi, \u201cWiFi Based Indoor Navigation System for Closed Buildings Using Smart Phones,\u201d International Journal of Engineering and Manufacturing, vol.11, no.3, pp.35-44, 2021. 10.5815\/ijem.2021.03.04","DOI":"10.5815\/ijem.2021.03.04"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] A.A. Morgan, G.S.B. Humaid, and A.I. 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Wang, \u201cAn Intelligent Geomagnetic Search Navigation Method Based on Evolutionary Gradient Strategy,\u201d J. Phys.: Conf. Ser., vol.1885, no.4, 042004, 2021. 10.1088\/1742-6596\/1885\/4\/042004","DOI":"10.1088\/1742-6596\/1885\/4\/042004"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] J. Chen, S. Song, and H. Yu, \u201cAn indoor multi-source fusion positioning approach based on PDR\/MM\/WiFi,\u201d AEU-International Journal of Electronics and Communications, vol.135, 153733, 2021. 10.1016\/j.aeue.2021.153733","DOI":"10.1016\/j.aeue.2021.153733"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] J. Wang and J. Park, \u201cAn Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN,\u201d Sensors, vol.21, no.8, 2769, 2021. 10.3390\/s21082769","DOI":"10.3390\/s21082769"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] M. Seifeldin, A. Saeed, A.E. Kosba, A. El-Keyi, and M. 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Tao and L. Zhao, \u201cA Novel System for WiFi Radio Map Automatic Adaptation and Indoor Positioning,\u201d IEEE Trans. Veh. Technol., vol.67, no.11, pp.10683-10692, 2018. 10.1109\/tvt.2018.2867065","DOI":"10.1109\/TVT.2018.2867065"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] M. Alfakih, M. Keche, H. Benoudnine, and A. Meche, \u201cImproved Gaussian mixture modeling for accurate Wi-Fi based indoor localization systems,\u201d Physical Communication, vol.43, 101218, 2020. 10.1016\/j.phycom.2020.101218","DOI":"10.1016\/j.phycom.2020.101218"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] H.A. Al-Jamimi and A. 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Huang, \u201cHigh-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point,\u201d Sensors, vol.19, no.10, 2324, 2019. 10.3390\/s19102324","DOI":"10.3390\/s19102324"},{"key":"23","unstructured":"[23] J. Liu, H. Zhou, and C. Chen, \u201cA novel interpolation fingerprint localization supported by back propagation neural network,\u201d Sensors & Transducers, vol.158, no.11, 338, 2013."},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] I. Ashraf, Y.B. Zikria, S. Hur, et al., Localizing pedestrians in indoor environments using magnetic field data with term frequency paradigm and deep neural networks[J]. International Journal of Machine Learning and Cybernetics, Int. J. Mach. Learn. & Cyber., vol.12, no.11, pp.3203-3219, 2021. 10.1007\/s13042-021-01279-8","DOI":"10.1007\/s13042-021-01279-8"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] N. Langren\u00e9 and X. 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