{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:18:11Z","timestamp":1760239091875,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Due to the explosive development of location-based services (LBS), localization has attracted significant research attention over the past decade. Among the associated techniques, wireless fingerprint positioning has garnered much interest due to its compatibility with existing hardware. At present, with the widespread deployment of long-term evolution (LTE) networks and the uniqueness of wireless information fingerprints, fingerprint positioning based on LTE networks is the mainstream method for outdoor positioning. However, in order to improve its accuracy, this method needs to collect enough data at a large number of reference points, which is a labor-intensive task. In this paper, experimental data are collected at different reference points and then converted into wavelet feature maps. Then, a Deep Convolutional Generative Adversarial Network (DCGAN) is leveraged to generate a symmetric fingerprint database. Localization is then carried out by the proposed Deep Residual Network (Resnet), which is capable of learning reliable features from a fingerprint image database. To further increase the robustness of the positioning system, a variety of data enhancement methods are used. Finally, we experimentally demonstrate that the generated symmetric fingerprint database and proposed Resnet reduce the manpower required for fingerprint database collection and improve the accuracy of the outdoor positioning system.<\/jats:p>","DOI":"10.3390\/sym12091565","type":"journal-article","created":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T03:03:39Z","timestamp":1600916619000},"page":"1565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Wavelet Feature Outdoor Fingerprint Localization Based on ResNet and Deep Convolution GAN"],"prefix":"10.3390","volume":"12","author":[{"given":"Yingke","family":"Lei","sequence":"first","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"},{"name":"Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Da","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"},{"name":"Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haichuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17489725.2018.1508763","article-title":"Location based services: Ongoing evolution and research agenda","volume":"12","author":"Huang","year":"2018","journal-title":"J. Locat. Based Serv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.24086\/cuesj.v3n2y2019.pp42-47","article-title":"Survey on Wireless Indoor Positioning Systems","volume":"3","author":"Ali","year":"2019","journal-title":"Cihan Univ. Erbil Sci. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/COMST.2015.2448632","article-title":"A survey of fingerprint-based outdoor localization","volume":"18","author":"Vo","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_4","unstructured":"Niu, J., Lu, B., Cheng, L., Gu, Y., and Shu, L. (2013, January 7\u201310). Ziloc: Energy efficient wifi fingerprint-based localization with low-power radio. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11276-006-0725-7","article-title":"The Horus location determination system","volume":"14","author":"Youssef","year":"2008","journal-title":"Wirel. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.sigpro.2016.07.005","article-title":"Wireless RSSI fingerprinting localization","volume":"131","author":"Yiu","year":"2017","journal-title":"Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.pmcj.2011.04.008","article-title":"Design and implementation of a self-guided indoor robot based on a two-tier localization architecture","volume":"8","author":"Yeh","year":"2012","journal-title":"Pervasive Mob. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kessel, M., and Werner, M. (2012, January 13\u201315). Automated WLAN calibration with a backtracking particle filter. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia.","DOI":"10.1109\/IPIN.2012.6418907"},{"key":"ref_9","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1109\/TMTT.2009.2035945","article-title":"Real-time noncoherent UWB positioning radar with millimeter range accuracy: Theory and experiment","volume":"58","author":"Zhang","year":"2009","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_11","unstructured":"Ni, L.M., Liu, Y., Lau, Y.C., and Patil, P.A. (2003, January 26). LANDMARC: Indoor location sensing using active RFID. Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, Fort Worth, TX, USA."},{"key":"ref_12","first-page":"647","article-title":"RSSI-based anti-interference WSN positioning algorithm","volume":"31","author":"Xu","year":"2010","journal-title":"J. Northeast. Univ. Nat. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/MAES.2013.6575420","article-title":"Review of range-based positioning algorithms","volume":"28","author":"Yan","year":"2013","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.comcom.2015.09.022","article-title":"Reducing fingerprint collection for indoor localization","volume":"83","author":"Gu","year":"2016","journal-title":"Comput. Commun."},{"key":"ref_15","unstructured":"Honkela, T., Duch, W., Girolami, M., and Kaski, S. (2011, January 17\u201319). Semi-supervised learning for wlan positioning. Proceedings of the ICANN: International Conference on Artificial Neural Networks, Munich, Germany."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mirowski, P., Ho, T.K., Yi, S., and MacDonald, M. (2013, January 28\u201331). SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Montbeliard-Belfort, France.","DOI":"10.1109\/IPIN.2013.6817853"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1109\/70.938381","article-title":"A solution to the simultaneous localization and map building (SLAM) problem","volume":"17","author":"Newman","year":"2001","journal-title":"IEEE Trans. Robot. Autom."},{"key":"ref_18","unstructured":"Bahl, P., and Padmanabhan, V.N. (2000, January 26\u201330). RADAR: An in-building RF-based user location and tracking system. Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, Tel Aviv, Israel."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.comnet.2004.09.004","article-title":"Statistical learning theory for location fingerprinting in wireless LANs","volume":"47","author":"Brunato","year":"2005","journal-title":"Comput. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6155","DOI":"10.3390\/s120506155","article-title":"Using LS-SVM based motion recognition for smartphone indoor wireless positioning","volume":"12","author":"Pei","year":"2012","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12071","DOI":"10.1109\/ACCESS.2017.2712131","article-title":"Neural-network-assisted UE localization using radio-channel fingerprints in LTE networks","volume":"5","author":"Ye","year":"2017","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Dai, B., Wan, X., and Li, W.X. (2019). Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network. Sensors, 19.","DOI":"10.3390\/s19204597"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1190\/1.2127113","article-title":"Spectral decomposition of seismic data with continuous-wavelet transform","volume":"70","author":"Sinha","year":"2005","journal-title":"Geophysics"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, Q., Qu, H., Liu, Z., Sun, W., Shao, X., and Li, J. (2018, January 9\u201313). Wavelet Transform DC-GAN for Diversity Promoted Fingerprint Construction in Indoor Localization. Proceedings of the 2018 IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, UAE.","DOI":"10.1109\/GLOCOMW.2018.8644149"},{"key":"ref_25","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/TIFS.2018.2871749","article-title":"Deep residual network for steganalysis of digital images","volume":"14","author":"Boroumand","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Berruet, B., Baala, O., Caminada, A., and Guillet, V. (2018, January 24\u201327). DelFin A Deep Learning Based CSI Fingerprinting Indoor Localization in IoT Context. Proceedings of the 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, France.","DOI":"10.1109\/IPIN.2018.8533777"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/9\/1565\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:12:16Z","timestamp":1760177536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/9\/1565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,22]]},"references-count":27,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["sym12091565"],"URL":"https:\/\/doi.org\/10.3390\/sym12091565","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,9,22]]}}}