{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:23:20Z","timestamp":1778347400561,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,8,31]],"date-time":"2018-08-31T00:00:00Z","timestamp":1535673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>WiFi fingerprinting indoor positioning systems have extensive applied prospects. However, a vast amount of data in a particular environment has to be gathered to establish a fingerprinting database. Deficiencies of these systems are the lack of universality of multipath effects and a burden of heavy workload on fingerprint storage. Thus, this paper presents a novel Random Forest fingerprinting localization (RFFP) method using channel state information (CSI), which utilizes the Random Forest model trained in the offline stage as fingerprints in order to economize memory space and possess a good anti-multipath characteristic. Furthermore, a series of specific experiments are conducted in a microwave anechoic chamber and an office to detail the localization performance of RFFP with different wireless channel circumstances, system parameters, algorithms, and input datasets. In addition, compared with other algorithms including K-Nearest-Neighbor (KNN), Weighted K-Nearest-Neighbor (WKNN), REPTree, CART, and J48, the RFFP method provides far greater classification accuracy as well as lower mean location error. The proposed method offers outstanding comprehensive performance including accuracy, robustness, low workload, and better anti-multipath-fading.<\/jats:p>","DOI":"10.3390\/s18092869","type":"journal-article","created":{"date-parts":[[2018,8,31]],"date-time":"2018-08-31T10:57:52Z","timestamp":1535713072000},"page":"2869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":108,"title":["WiFi Indoor Localization with CSI Fingerprinting-Based Random Forest"],"prefix":"10.3390","volume":"18","author":[{"given":"Yanzhao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chundi","family":"Xiu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuanli","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongkai","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Buchli, B., Sutton, F., and Beutel, J. (2012). GPS-Equipped Wireless Sensor Network Node for High-Accuracy Positioning Applications. European Conference on Wireless Sensor Networks, Springer.","DOI":"10.1007\/978-3-642-28169-3_12"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.3390\/s150101804","article-title":"A fast and precise indoor localization algorithm based on an online sequential extreme learning machine","volume":"15","author":"Zou","year":"2015","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1109\/TSMCC.2007.905750","article-title":"Survey of wireless indoor positioning techniques and systems","volume":"37","author":"Liu","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. C Appl. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/SURV.2009.090103","article-title":"A survey of indoor positioning systems for wireless personal networks","volume":"11","author":"Gu","year":"2009","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1016\/j.comcom.2012.06.004","article-title":"A survey of active and passive indoor localisation systems","volume":"35","author":"Deak","year":"2012","journal-title":"Comput. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/JSYST.2014.2346625","article-title":"Real-time locating systems using active rfid for internet of things","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE Syst. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1023\/B:WINE.0000044029.06344.dd","article-title":"Landmarc: Indoor location sensing using active rfid","volume":"10","author":"Ni","year":"2004","journal-title":"Wirel. Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/35.983917","article-title":"Indoor geolocation science and technology","volume":"40","author":"Pahlavan","year":"2002","journal-title":"IEEE Commun. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Yang, J., You, L., Qi, L., and Naser, E.S. (2016). Smartphone-based indoor localization with bluetooth low energy beacons. Sensors, 16.","DOI":"10.3390\/s16050596"},{"key":"ref_10","first-page":"474","article-title":"Modeling and simulation of ultra wideband indoor localization systems in soft non-line-of-sight","volume":"117","author":"Senger","year":"2012","journal-title":"J. Am. Chem. Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/JSEN.2016.2639530","article-title":"Robust biomechanical model-based 3D indoor localization and tracking method using UWB and IMU","volume":"17","author":"Yoon","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, T., Li, G., Li, J., and Zhang, Y. (2016, January 6\u201310). Geodetic coordinate calculation based on monocular vision on UAV platform. Proceedings of the International Conference on Signal Processing (ICSP), Chengdu, China.","DOI":"10.1109\/ICSP.2016.7877846"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1109\/JIOT.2015.2495229","article-title":"A Feature-Scaling-Based k-Nearest Neighbor Algorithm for Indoor Positioning Systems","volume":"3","author":"Li","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Farjow, W., Chehri, A., Hussein, M., and Fernando, X. (2011, January 28\u201331). Support Vector Machines for indoor sensor localization. Proceedings of the Wireless Communications and NETWORKING Conference, Cancun, Mexico.","DOI":"10.1109\/WCNC.2011.5779231"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/TMC.2007.42","article-title":"Location Estimation via Support Vector Regression","volume":"6","author":"Wu","year":"2007","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/34.58871","article-title":"Neural network ensembles","volume":"12","author":"Hansen","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/TCYB.2016.2623900","article-title":"A novel AdaBoost framework with robust threshold and structural optimization","volume":"48","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Song, C.J., and Wang, J. (2017). WLAN Fingerprint Indoor Positioning Strategy Based on Implicit Crowdsourcing and Semi-Supervised Learning. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110356"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"403242","DOI":"10.1155\/2015\/403242","article-title":"Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks","volume":"11","author":"Bernas","year":"2015","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5714","DOI":"10.1109\/TVT.2015.2463716","article-title":"Nonorthogonal Time-Frequency Training-Sequence-Based CSI Acquisition for MIMO Systems","volume":"65","author":"Ding","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Drakshayini, M.N., and Singh, A.V. (2016, January 21\u201323). A review on reconfigurable orthogonal frequency division multiplexing (OFDM) system for wireless communication. Proceedings of the International Conference on Applied and Theoretical Computing and Communication Technology, Bangalore, India.","DOI":"10.1109\/ICATCCT.2016.7911969"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/TPDS.2012.214","article-title":"Csi-based indoor localization","volume":"24","author":"Wu","year":"2013","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7787","DOI":"10.1109\/JSEN.2016.2602840","article-title":"Channel State Reconstruction Using Multilevel Discrete Wavelet Transform for Improved Fingerprinting-Based Indoor Localization","volume":"16","author":"Fang","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_25","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 International Conference on Wireless Communications & Signal Processing, Yangzhou, China.","DOI":"10.1109\/WCSP.2016.7752710"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1049\/iet-com.2016.0562","article-title":"NLOS identification and mitigation based on channel state information for indoor WiFi localisation","volume":"11","author":"Li","year":"2017","journal-title":"IET Commun."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Berkvens, R., Peremans, H., and Weyn, M. (2016). Conditional Entropy and Location Error in Indoor Localization Using Probabilistic Wi-Fi Fingerprinting. Sensors, 16.","DOI":"10.3390\/s16101636"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Torressospedra, J., and Moreira, A. (2017). Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting. Sensors, 17.","DOI":"10.3390\/s17122736"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1862","DOI":"10.1109\/TPAMI.2014.2382106","article-title":"Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting","volume":"37","author":"Lindner","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1109\/TIP.2012.2222895","article-title":"High-Order Local Spatial Context Modeling by Spatialized Random Forest","volume":"22","author":"Ni","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","first-page":"763","article-title":"Csi-based fingerprinting for indoor localization: A deep learning approach","volume":"66","author":"Wang","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/TST.2016.7536719","article-title":"Taiga: Performance Optimization of the C4.5 Decision Tree Construction Algorithm","volume":"21","author":"Yang","year":"2016","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1109\/ACCESS.2017.2656618","article-title":"Cooperative profit random forests with application in ocean front recognition","volume":"5","author":"Sun","year":"2017","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/TIP.2014.2378017","article-title":"Random forest construction with robust semisupervised node splitting","volume":"24","author":"Liu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1109\/TKDE.2007.44","article-title":"K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods","volume":"19","author":"Gaddam","year":"2007","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_37","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Routledge."},{"key":"ref_38","unstructured":"Zhao, S., Liu, Y., Jiang, J., Cheng, W., Zhou, M., Li, M., and Yuan, R. (2014, January 25\u201327). Extraction of mangrove in Hainan Dongzhai Harbor based on CART decision tree. Proceedings of the 2014 22nd International Conference on Geoinformatics, Kaohsiung, Taiwan."},{"key":"ref_39","first-page":"708","article-title":"Cart-based Land Use\/cover Classification of Remote Sensing Images","volume":"9","author":"Zhao","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Deng, H.W., and Zhang, J.G. (2007). Gene selection for classification of microarray data based on the Bayes error. BMC Bioinform., 8.","DOI":"10.1186\/1471-2105-8-370"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shah, S.A.A., Aziz, W., Arif, M., and Nadeem, M.S.A. (2015, January 14\u201316). Decision Trees Based Classification of Cardiotocograms Using Bagging Approach. Proceedings of the Frontiers of Information Technology, Islamabad, Pakistan.","DOI":"10.1109\/FIT.2015.14"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1080\/01621459.1992.10475276","article-title":"The Little Bootstrap and other Methods for Dimensionality Selection in Regression: X-Fixed Prediction Error","volume":"87","author":"Breiman","year":"1992","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_43","first-page":"1","article-title":"Induction of Oblique Decision Trees","volume":"2","author":"Heath","year":"1993","journal-title":"J. Artif. Intell. Res."},{"key":"ref_44","unstructured":"Dinh-Van, N., Nashashibi, F., Thanh-Huong, N., and Castelli, E. (2017, January 19\u201321). Indoor Intelligent Vehicle localization using WiFi received signal strength indicator. Proceedings of the IEEE Mtt-S International Conference on Microwaves for Intelligent Mobility, Nagoya, Japan."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/2869\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:22:16Z","timestamp":1760196136000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/9\/2869"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,31]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["s18092869"],"URL":"https:\/\/doi.org\/10.3390\/s18092869","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,31]]}}}