{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:26:48Z","timestamp":1774024008020,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific and Technological Research Program of Chongqing Municipal Education Commission","award":["KJZD-M202400602"],"award-info":[{"award-number":["KJZD-M202400602"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.<\/jats:p>","DOI":"10.3390\/s24185940","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T04:25:22Z","timestamp":1726201522000},"page":"5940","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0563-6935","authenticated-orcid":false,"given":"Chengjie","family":"Hou","sequence":"first","affiliation":[{"name":"School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400000, China"}]},{"given":"Zhizhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s11277-021-08209-5","article-title":"A review of indoor localization techniques and wireless technologies","volume":"119","author":"Obeidat","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s10291-021-01106-1","article-title":"Real-time GNSS precise point positioning for low-cost smart devices","volume":"25","author":"Wang","year":"2021","journal-title":"GPS Solut."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1049\/iet-com.2019.1059","article-title":"A Survey on WiFi-Based Indoor Positioning Technologies","volume":"14","author":"Liu","year":"2020","journal-title":"IET Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1109\/TMC.2017.2737004","article-title":"Automatic Radio Map Adaptation for Indoor Localization Using Smartphones","volume":"17","author":"Wu","year":"2018","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1109\/JIOT.2016.2609405","article-title":"Robust Cooperative Wi-Fi Fingerprint-Based Indoor Localization","volume":"3","author":"Chen","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7314","DOI":"10.1109\/TVT.2018.2833029","article-title":"Accurate WiFi Localization by Fusing a Group of Fingerprints via Global Fusion Profile","volume":"67","author":"Guo","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mariakakis, A.T., Sen, S., Lee, J., and Kim, K.H. (2014, January 16\u201319). SAIL: Single Access Point-Based Indoor Localization. Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, New York, NY, USA.","DOI":"10.1145\/2594368.2594393"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2480","DOI":"10.1109\/JLT.2014.2327623","article-title":"Three-Dimensional Visible Light Indoor Localization Using AOA and RSS With Multiple Optical Receivers","volume":"32","author":"Yang","year":"2014","journal-title":"J. Light. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Schmitz, J., Hernandez, M., and Mathar, R. (2016, January 11\u201314). Demonstration Abstract: Real-Time Indoor Localization with TDOA and Distributed Software Defined Radio. Proceedings of the 2016 15th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Vienna, Austria.","DOI":"10.1109\/IPSN.2016.7460671"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4498","DOI":"10.1109\/JIOT.2020.3026608","article-title":"Indoor Fingerprinting With Bimodal CSI Tensors: A Deep Residual Sharing Learning Approach","volume":"8","author":"Wang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"66213","DOI":"10.1109\/ACCESS.2020.2985384","article-title":"MINLOC:Magnetic Field Patterns-Based Indoor Localization Using Convolutional Neural Networks","volume":"8","author":"Ashraf","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rexhausen, T., Chen, C.S., and Pianese, F. (2021, January 1\u20132). Indoor Localization Using Multi-Color Fingerprinting. Proceedings of the 2020 IEEE International Conference on E-health Networking, Application Services (HEALTHCOM), Virtually.","DOI":"10.1109\/HEALTHCOM49281.2021.9398986"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"291","DOI":"10.23919\/JCC.2022.04.021","article-title":"An improved convolutional neural network based indoor localization by using Jenks natural breaks algorithm","volume":"19","author":"Hou","year":"2022","journal-title":"China Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"30591","DOI":"10.1109\/ACCESS.2020.2973212","article-title":"A Novel Weighted KNN Algorithm Based on RSS Similarity and Position Distance for Wi-Fi Fingerprint Positioning","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1109\/TMC.2015.2463287","article-title":"Tilejunction: Mitigating Signal Noise for Fingerprint-Based Indoor Localization","volume":"15","author":"He","year":"2016","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7153","DOI":"10.1109\/JSEN.2022.3229476","article-title":"FCLoc: A Novel Indoor Wi-Fi Fingerprints Localization Approach to Enhance Robustness and Positioning Accuracy","volume":"23","author":"Hou","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_17","unstructured":"Bahl, P., and Padmanabhan, V. (2000, January 26\u201330). RADAR: An in-building RF-based user location and tracking system. Proceedings of the IEEE INFOCOM, Tel Aviv, Israel."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11276-006-0725-7","article-title":"The Horus location determination system","volume":"14","author":"Moustafa","year":"2008","journal-title":"Wirel. Netw."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gholoobi, A., and Stavrou, S. (2015, January 3\u20135). RSS Based Localization Using a New WKNN Approach. Proceedings of the 2015 7th International Conference on Computational Intelligence, Communication Systems and Networks, Riga, Latvia.","DOI":"10.1109\/CICSyN.2015.15"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TNSE.2018.2871165","article-title":"Deep Convolutional Neural Networks for Indoor Localization with CSI Images","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"18066","DOI":"10.1109\/ACCESS.2017.2749516","article-title":"ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Liu, X., Liu, Z., and Xu, Z. (2019, January 29\u201331). MFMCF: A Novel Indoor Location Method Combining Multiple Fingerprints and Multiple Classifiers. Proceedings of the 2019 3rd International Symposium on Autonomous Systems (ISAS), Shanghai, China.","DOI":"10.1109\/ISASS.2019.8757788"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, L., Leung, Y.W., Chu, X., and Ng, J.K.Y. (2020, January 7\u201311). Multi-Fingerprint for Wireless Localization in Time-Varying Indoor Environment. Proceedings of the GLOBECOM 2020-2020 IEEE Global Communications Conference, Virtually.","DOI":"10.1109\/GLOBECOM42002.2020.9348052"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24440","DOI":"10.1109\/JSEN.2021.3113376","article-title":"An Accurate WiFi Indoor Positioning Algorithm for Complex Pedestrian Environments","volume":"21","author":"Yu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TMC.2011.30","article-title":"Principal Component Localization in Indoor WLAN Environments","volume":"11","author":"Fang","year":"2012","journal-title":"IEEE Trans. Mobile Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cand\u00e9s, E., Li, X., Ma, Y., and Wright, J. (2010, January 4\u20137). Robust principal component analysis?: Recovering low-rank matrices from sparse errors. Proceedings of the 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, Jerusalem, Israel.","DOI":"10.1109\/SAM.2010.5606734"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, L., Tan, T., Gong, Y., and Yang, W. (2019). Fingerprint Database Reconstruction Based on Robust PCA for Indoor Localization. Sensors, 19.","DOI":"10.3390\/s19112537"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3053","DOI":"10.1109\/JIOT.2020.2964875","article-title":"TILoc: Improving the Robustness and Accuracy for Fingerprint-Based Indoor Localization","volume":"7","author":"Li","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_29","unstructured":"Lin, Z., Chen, M., and Ma, Y. (2010). The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Nowicki, M., and Wietrzykowski, J. (2016). Low-effort place recognition with WiFi fingerprints using deep learning. arXiv.","DOI":"10.1007\/978-3-319-54042-9_57"},{"key":"ref_31","unstructured":"Fazel, M. (2001). Matrix Rank Minimization with Applications. [Ph.D. Thesis, Infomation Systems Lab, Electrical Engineering Department, Stanford University]."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1137\/120863290","article-title":"Augmented L1 and Nuclear-Norm models with a globally linearly convergent algorithm","volume":"6","author":"Lai","year":"2013","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1016003126882","article-title":"A probabilistic approach to WLAN user location estimation","volume":"9","author":"Roos","year":"2002","journal-title":"Int. J. Wirel. Inf. Netw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/5940\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:55:24Z","timestamp":1760111724000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/5940"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":33,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24185940"],"URL":"https:\/\/doi.org\/10.3390\/s24185940","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,13]]}}}