{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:19:13Z","timestamp":1761581953214,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,20]],"date-time":"2020-04-20T00:00:00Z","timestamp":1587340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Wi-Fi and magnetic field fingerprinting-based localization have gained increased attention owing to their satisfactory accuracy and global availability. The common signal-based fingerprint localization deteriorates due to well-known signal fluctuations. In this paper, we proposed a Wi-Fi and magnetic field-based localization system based on deep learning. Owing to the low discernibility of magnetic field strength (MFS) in large areas, the unsupervised learning density peak clustering algorithm based on the comparison distance (CDPC) algorithm is first used to pick up several center points of MFS as the geotagged features to assist localization. Considering the state-of-the-art application of deep learning in image classification, we design a location fingerprint image using Wi-Fi and magnetic field fingerprints for localization. Localization is casted in a proposed deep residual network (Resnet) that is capable of learning key features from a massive fingerprint image database. To further enhance localization accuracy, by leveraging the prior information of the pre-trained Resnet coarse localizer, an MLP-based transfer learning fine localizer is introduced to fine-tune the coarse localizer. Additionally, we dynamically adjusted the learning rate (LR) and adopted several data enhancement methods to increase the robustness of our localization system. Experimental results show that the proposed system leads to satisfactory localization performance both in indoor and outdoor environments.<\/jats:p>","DOI":"10.3390\/ijgi9040267","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T03:23:06Z","timestamp":1587439386000},"page":"267","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Deep Learning for Fingerprint Localization in Indoor and Outdoor Environments"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6683-8258","authenticated-orcid":false,"given":"Da","family":"Li","sequence":"first","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China"},{"name":"Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingke","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China"},{"name":"Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haichuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,20]]},"reference":[{"key":"ref_1","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":"2016","journal-title":"Signal. Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.comnet.2016.10.006","article-title":"Advances on localization techniques for wireless sensor networks: A survey","volume":"110","author":"Chowdhury","year":"2016","journal-title":"Comput. Netw."},{"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":"Shaochuan, W., Yuze, W., and Wen, C. (2013, January 23\u201324). A Gossip-based AOA Distributed Localization Algorithm for Wireless Sensor Networks. Proceedings of the IEEE International Symposium on Instrumentation & Measurement, Toronto, ON, Canada."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"74699","DOI":"10.1109\/ACCESS.2018.2884193","article-title":"Indoor positioning based on fingerprint-image and deep learning","volume":"6","author":"Shao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_6","unstructured":"Shin, B., Lee, J.H., Lee, T., and Kim, H.S. (2012, January 24\u201326). Enhanced weighted K-nearest neighbor algorithm for indoor Wi-Fi positioning systems. Proceedings of the 8th International Conference on Computing Technology and Information Management (NCM and ICNIT), Seoul, Korea."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, W., Fu, X., Deng, Z., Xu, L., and Jiao, J. (2016, January 4\u20137). Smallest enclosing circle-based fingerprint clustering and modified-WKNN matching algorithm for indoor positioning. Proceedings of the 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, Spain.","DOI":"10.1109\/IPIN.2016.7743694"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/LWC.2015.2482971","article-title":"FinCCM: Fingerprint crowdsourcing, clustering and matching for indoor subarea localization","volume":"4","author":"Chen","year":"2015","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.neucom.2015.03.099","article-title":"Distributed efficient localization in swarm robotic systems using swarm intelligence algorithms","volume":"172","author":"Nedjah","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.neucom.2016.02.055","article-title":"Deep neural networks for wireless localization in indoor and outdoor environments","volume":"194","author":"Zhang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, R., Li, Z., Luo, H., Zhao, F., Shao, W., and Wang, Q. (2019). A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron. Remote Sens., 11.","DOI":"10.3390\/rs11111293"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xia, S., Liu, Y., Yuan, G., Zhu, M., and Wang, Z. (2017). Indoor fingerprint positioning based on Wi-Fi: An overview. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050135"},{"key":"ref_14","first-page":"2043","article-title":"Improved indoor localization based on received signal strength indicator and general regression neural network","volume":"31","author":"Xu","year":"2019","journal-title":"Sens. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.comnet.2018.06.017","article-title":"Recent advances on cooperative wireless localization and their application in inhomogeneous propagation environments","volume":"142","author":"Li","year":"2018","journal-title":"Comput. Netw."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, Z., Gao, N., Xiao, Y., Meng, Z., and Li, Z. (2020). Cost-Effective Wearable Indoor Localization and Motion Analysis via the Integration of UWB and IMU. Sensors, 20.","DOI":"10.3390\/s20020344"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1109\/TITS.2016.2594479","article-title":"Performance evaluation of radio map construction methods for Wi-Fi positioning systems","volume":"18","author":"Jung","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1109\/TIM.2016.2566759","article-title":"A novel indoor positioning technique using magnetic fingerprint difference","volume":"65","author":"Kim","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tran, D.A., Gong, S., and Vo, Q. (2017, January 8\u201313). Geometric-based KNN localization using sensor dissimilarity information. Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292622"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, S., Han, R., Huang, W., Wang, S., and Hao, Q. (2018, January 28\u201331). Linear Bayesian Filter Based Low-Cost UWB Systems for Indoor Mobile Robot Localization. Proceedings of the 2018 IEEE SENSORS, New Delhi, India.","DOI":"10.1109\/ICSENS.2018.8589829"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4279","DOI":"10.1109\/TIE.2017.2764861","article-title":"Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems","volume":"65","author":"Kang","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chao, Y.W., Vijayanarasimhan, S., Seybold, B., David, A.R., Deng, J., and Rahul, K. (2018, January 18\u201323). Rethinking the faster r-cnn architecture for temporal action localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00124"},{"key":"ref_23","first-page":"763","article-title":"CSI-based fingerprinting for indoor localization: A deep learning approach","volume":"66","author":"Wang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Adege, A.B., Lin, H.P., Tarekegn, G.B., Munaye, Y.Y., and Yen, L. (2018). An indoor and outdoor positioning using a hybrid of support vector machine and deep neural network algorithms. J. Sens.","DOI":"10.1155\/2018\/1253752"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Z., Dai, B., Wan, X., and Li, X. (2019). Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network. Sensors, 19.","DOI":"10.3390\/s19204597"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.cosrev.2018.09.001","article-title":"Indoor location identification technologies for real-time IoT-based applications: An inclusive survey","volume":"30","author":"Oguntala","year":"2018","journal-title":"Comput. Sci. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.neucom.2016.01.102","article-title":"Clustering by fast search and find of density peaks via heat diffusion","volume":"208","author":"Mehmood","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/267\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:06Z","timestamp":1760364546000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/267"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,20]]},"references-count":29,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["ijgi9040267"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9040267","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2020,4,20]]}}}