{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T04:30:28Z","timestamp":1768278628918,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,7]],"date-time":"2019-07-07T00:00:00Z","timestamp":1562457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51874302"],"award-info":[{"award-number":["51874302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51674255"],"award-info":[{"award-number":["51674255"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xuzhou Applied Basic Research Program","award":["KC18061"],"award-info":[{"award-number":["KC18061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.<\/jats:p>","DOI":"10.3390\/s19132998","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T03:01:31Z","timestamp":1562554891000},"page":"2998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A CSI-Based Indoor Fingerprinting Localization with Model Integration Approach"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3296-4256","authenticated-orcid":false,"given":"Yuqing","family":"Yin","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China"},{"name":"Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changze","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China"},{"name":"Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8121-3264","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China"},{"name":"Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221000, China"},{"name":"Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gu, Y., Wada, Y., Hsu, L., and Kamijo, S. (2014, January 3\u20137). Vehicle self-localization in urban canyon using 3D map based GPS positioning and vehicle sensors. Proceedings of the 2014 International Conference on Connected Vehicles and Expo (ICCVE), Vienna, Austria.","DOI":"10.1109\/ICCVE.2014.7297660"},{"key":"ref_2","unstructured":"Zhou, Z., Yang, Z., Wu, C., Liu, Y., and Ni, L.M. (July, January 29). On Multipath Link Characterization and Adaptation for Device-Free Human Detection. Proceedings of the 35th IEEE International Conference on Distributed Computing Systems (ICDCS), Columbus, OH, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2418","DOI":"10.1109\/JSAC.2015.2430281","article-title":"Location Fingerprinting With Bluetooth Low Energy Beacons","volume":"33","author":"Faragher","year":"2015","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/COMST.2015.2464084","article-title":"Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons","volume":"18","author":"He","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gong, L., Yang, W., Xiang, C., Man, D., Yu, M., and Yin, Z. (2016, January 23\u201326). WiSal: Ubiquitous WiFi-Based Device-Free Passive Subarea Localization without Intensive Site-Survey. Proceedings of the IEEE TrustCom\/BigDataSE\/ISPA, Tianjin, China.","DOI":"10.1109\/TrustCom.2016.0185"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, J., Li, Y., and Ji, X. (2016, January 13\u201315). A novel method of Wi-Fi indoor localization based on channel state information. Proceedings of the 8th International Conference on Wireless Communications and Signal Processing (WCSP), Yangzhou, China.","DOI":"10.1109\/WCSP.2016.7752710"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1145\/1851275.1851203","article-title":"Predictable 802.11 packet delivery from wireless channel measurements","volume":"40","author":"Halperin","year":"2011","journal-title":"ACM Sigcomm. Comput. Commun. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1109\/TIE.2014.2327595","article-title":"Indoor Localization Based on Curve Fitting and Location Search Using Received Signal Strength","volume":"62","author":"Wang","year":"2014","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","unstructured":"Rehim, M.A.A.A.Y.A. (2004). Horus: A Wlan-Based Indoor Location Determination System, University of Maryland at College Park."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.comcom.2017.10.013","article-title":"Exploiting Distribution of Channel State Information for Accurate Wireless Indoor Localization","volume":"114","author":"Xiao","year":"2017","journal-title":"Comput. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1145\/1925861.1925870","article-title":"Tool release: Gathering 802.11n traces with channel state information","volume":"41","author":"Halperin","year":"2011","journal-title":"ACM Sigcomm. Comput. Commun. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.neucom.2014.07.059","article-title":"Hierarchical retinal blood vessel segmentation based on feature and ensemble learning","volume":"149","author":"Wang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1109\/TASLP.2015.2505415","article-title":"Boosting contextual information for deep neural network based voice activity detection","volume":"24","author":"Zhang","year":"2016","journal-title":"IEEE\/ACM Trans. Audio, Speech Lang. Process. (TASLP)"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Conti, M. (2017). Real Time Localization Using Bluetooth Low Energy. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain, 26\u201328 April 2017, Springer LNCS.","DOI":"10.1007\/978-3-319-56154-7_52"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1109\/JSAC.2015.2430274","article-title":"Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing","volume":"33","author":"Shu","year":"2015","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, H., Yang, W., Wang, J., Xu, Y., and Huang, L. (2016, January 12\u201316). WiFinger: Talk to your smart devices with finger-grained gesture. Proceedings of the UbiComp 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971738"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/2534169.2486039","article-title":"See through walls with WiFi!","volume":"43","author":"Adib","year":"2013","journal-title":"ACM Sigcomm. Conf. Sigcomm."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/JIOT.2016.2558659","article-title":"CSI Phase Fingerprinting for Indoor Localization with a Deep Learning Approach","volume":"3","author":"Wang","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, K., Xiao, J., Yi, Y., Gao, M., and Ni, L.M. (2012, January 25\u201330). FILA: Fine-grained indoor localization. Proceedings of the 2012 IEEE INFOCOM, Orlando, FL, USA.","DOI":"10.1109\/INFCOM.2012.6195606"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xiao, J., Wu, K., Yi, Y., and Ni, L.M. (August, January 30). FIFS: Fine-Grained Indoor Fingerprinting System. Proceedings of the 2012 21st International Conference on Computer Communications and Networks (ICCCN), Munich, Germany.","DOI":"10.1109\/ICCCN.2012.6289200"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sen, S., Radunovic, B., Choudhury, R.R., and Minka, T. (2012, January 26\u201329). You Are Facing the Mona Lisa: Spot Localization Using PHY Layer Information. Proceedings of the 10th MobiSys \u201912 International Conference on Mobile Systems, Applications, and Services, Lake District, UK.","DOI":"10.1145\/2307636.2307654"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1145\/2543581.2543592","article-title":"From RSSI to CSI: Indoor localization via channel response","volume":"46","author":"Yang","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chapre, Y., Ignjatovic, A., Seneviratne, A., and Jha, S. (2014, January 8\u201311). CSI-MIMO: Indoor Wi-Fi fingerprinting system. Proceedings of the 39th Annual IEEE Conference on Local Computer Networks, Edmonton, AB, Canada.","DOI":"10.1109\/LCN.2014.6925773"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/JSAC.2003.810294","article-title":"Capacity limits of MIMO channels","volume":"21","author":"Goldsmith","year":"2003","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TMC.2016.2557792","article-title":"WiFall: Device-Free Fall Detection by Wireless Networks","volume":"16","author":"Wang","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ali, K., Liu, A.X., Wang, W., and Shahzad, M. (2015, January 7\u201311). Keystroke Recognition Using WiFi Signals. Proceedings of the MobiCom \u201915 21st Annual International Conference on Mobile Computing and Networking, Paris, France.","DOI":"10.1145\/2789168.2790109"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ertam, F., and Ayd\u0131n, G. (2017, January 5\u20137). Data classification with deep learning using Tensorflow. Proceedings of the 2nd International Conference on Computer Science and Engineering (UBMK), Antalya, Turkey.","DOI":"10.1109\/UBMK.2017.8093521"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","article-title":"Extreme learning machine: Theory and applications","volume":"70","author":"Huang","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., and Sander, J. (2000, January 16\u201318). LOF: Identifying density-based local outliers. Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA.","DOI":"10.1145\/342009.335388"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/2998\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:03:23Z","timestamp":1760187803000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/2998"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,7]]},"references-count":31,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19132998"],"URL":"https:\/\/doi.org\/10.3390\/s19132998","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,7]]}}}