{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:37:18Z","timestamp":1770917838018,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,24]],"date-time":"2020-05-24T00:00:00Z","timestamp":1590278400000},"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>Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.<\/jats:p>","DOI":"10.3390\/s20102981","type":"journal-article","created":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T11:42:02Z","timestamp":1590406922000},"page":"2981","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1509-9050","authenticated-orcid":false,"given":"Haotai","family":"Sun","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Xiaodong","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7926-7936","authenticated-orcid":false,"given":"Yuanning","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Wentao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1007\/978-3-540-30141-7_87","article-title":"A Location Estimation and Action Prediction System in a Wireless LAN Environment","volume":"3222","author":"Chai","year":"2004","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Salamah, A.H., Tamazin, M., Sharkas, M.A., Khedr, M., and Mahmoud, M. (2019). Comprehensive investigation on principle component large-scale Wi-Fi indoor localization. Sensors, 19.","DOI":"10.3390\/s19071678"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ali, M.U., Hur, S., and Park, Y. (2019). Wi-Fi-based effortless indoor positioning system using IoT sensors. Sensors, 19.","DOI":"10.3390\/s19071496"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15865","DOI":"10.1109\/ACCESS.2017.2737651","article-title":"Achieving cost-efficient indoor fingerprint localization on WLAN platform: A hypothetical test approach","volume":"5","author":"Zhou","year":"2017","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Caso, G., De Nardis, L., Lemic, F., Handziski, V., Wolisz, A., and Di Benedetto, M.-G. (2019). ViFi: Virtual fingerprinting wifi-based indoor positioning via multi-wall multi-floor propagation model. IEEE Trans. Mob. Comput., 19.","DOI":"10.1109\/TMC.2019.2908865"},{"key":"ref_6","first-page":"29","article-title":"Design and realize of information service platform based on LBS and SNS","volume":"2","author":"Lin","year":"2012","journal-title":"Geospat. Inf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bshara, M., and Van Biesen, L. (2009, January 24\u201328). Fingerprinting-based localization in WiMAX networks depending on SCORE measurements. Proceedings of the 2009 5th Advanced International Conference on Telecommunications, AICT 2009, Venice, Italy.","DOI":"10.1109\/AICT.2009.83"},{"key":"ref_8","first-page":"69","article-title":"Research on location service industry and development","volume":"38","author":"Yang","year":"2014","journal-title":"Mob. Commun."},{"key":"ref_9","unstructured":"Gleason, S., Gebre-Egziabher, D., and Gleason Scott, G.-E.D. (2009). GNSS applications and methods. Handb. Unmanned Aer. Veh., 347\u2013380."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4388","DOI":"10.1109\/ACCESS.2017.2678603","article-title":"Semi-Supervised Learning for Indoor Hybrid Fingerprint Database Calibration with Low Effort","volume":"5","author":"Zhou","year":"2017","journal-title":"IEEE Access"},{"key":"ref_11","first-page":"713","article-title":"Development of indoor positioning system by using the infrared rays data communication tags for pedestrian navigation","volume":"133","author":"Namie","year":"2013","journal-title":"IEEJ Trans. Electron. Inf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6375","DOI":"10.1109\/JSEN.2018.2846481","article-title":"Design of chirp waveforms for multiple-access ultrasonic indoor positioning","volume":"18","author":"Khyam","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, J., Ou, G., Peng, A., Zheng, L., and Shi, J. (2018). An INS\/WiFi Indoor localization system based on the weighted least squares. Sensors, 18.","DOI":"10.3390\/s18051458"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, C., Qin, N., Xue, Y., and Yang, L. (2020). Received signal strength-based indoor localization using hierarchical classification. Sensors, 20.","DOI":"10.3390\/s20041067"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Er Rida, M., Liu, F., Jadi, Y., Algawhari, A.A.A., and Askourih, A. (2015, January 24\u201326). Indoor location position based on bluetooth signal strength. Proceedings of the Proceedings-2015 2nd International Conference on Information Science and Control Engineering, ICISCE 2015, Shanghai, China.","DOI":"10.1109\/ICISCE.2015.177"},{"key":"ref_16","first-page":"1820","article-title":"Method of WiFi indoor location based on SVM","volume":"6","author":"Sang","year":"2014","journal-title":"Appl. Res. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1109\/TMC.2015.2451641","article-title":"Evaluation of two WiFi positioning systems based on autonomous crowdsourcing of handheld devices for indoor navigation","volume":"15","author":"Zhuang","year":"2016","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, P., Kuang, Y., and Chen, X. (2017). A UWB\/improved PDR integration algorithm applied to dynamic indoor positioning for pedestrians. Sensors, 17.","DOI":"10.3390\/s17092065"},{"key":"ref_19","first-page":"1","article-title":"An indoor location-based positioning system using stereo vision with the drone camera","volume":"2018","author":"Jin","year":"2018","journal-title":"Mob. Inf. Syst."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Schilit, B., Adams, N., and Want, R. (1994, January 8\u20139). Context-aware computing applications. Proceedings of the Mobile Computing Systems and Applications-Workshop Proceedings, Santa Cruz, CA, USA.","DOI":"10.1109\/WMCSA.1994.16"},{"key":"ref_22","first-page":"775","article-title":"RADAR: An in-building RF-based user location and tracking system","volume":"Volume 2","author":"Bahl","year":"2000","journal-title":"Proceedings of the Proceedings-IEEE INFOCOM"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ji, Y., Biaz, S., Pandey, S., and Agrawal, P. (2006, January 19\u201322). ARIADNE: A dynamic indoor signal map construction and localization system. Proceedings of the MobiSys 2006-Fourth International Conference on Mobile Systems, Applications and Services, Uppsala, Sweden.","DOI":"10.1145\/1134680.1134697"},{"key":"ref_24","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."},{"key":"ref_25","unstructured":"Li, B., Salter, J., Dempster, A.G., and Rizos, C. (2006). Indoor positioning techniques based on wireless LAN. Proceedings of the 1st IEEE International Conference on Wireless Broadband and Ultra Wideband Communications, AusWireless 2006, IEEE."},{"key":"ref_26","first-page":"574","article-title":"Enhanced weighted K-nearest neighbor algorithm for indoor Wi-Fi positioning systems","volume":"Volume 2","author":"Shin","year":"2012","journal-title":"Proceedings of the 2012 8th International Conference on Computing Technology and Information Management (NCM and ICNIT)"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.1109\/ACCESS.2017.2688362","article-title":"BiLoc: Bi-Modal deep learning for indoor localization with commodity 5GHz WiFi","volume":"5","author":"Wang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/JSTSP.2009.2029191","article-title":"Robust indoor positioning provided by real-time rssi values in unmodified WLAN networks","volume":"3","author":"Mazuelas","year":"2009","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Borenovic, M., and Neskovic, A. (2011, January 22\u201324). ANN based models for positioning in indoor WLAN environments. Proceedings of the 2011 19thTelecommunications Forum (TELFOR) Proceedings of Papers, Belgrade, Serbia.","DOI":"10.1109\/TELFOR.2011.6143551"},{"key":"ref_30","first-page":"62","article-title":"CSI indoor positioning based on Kmeans clustering","volume":"42","author":"Tian","year":"2016","journal-title":"Electron. Technol. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3996","DOI":"10.1109\/JSEN.2014.2356857","article-title":"Toward accurate human tracking: Modeling time-of-arrival for wireless wearable sensors in multipath environment","volume":"14","author":"He","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Alarifi, A., Al-Salman, A., Alsaleh, M., Alnafessah, A., Al-Hadhrami, S., Al-Ammar, M.A., and Al-Khalifa, H.S. (2016). Ultra wideband indoor positioning technologies: Analysis and recent advances. Sensors, 16.","DOI":"10.3390\/s16050707"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3328","DOI":"10.1109\/JSEN.2014.2386537","article-title":"Angle of arrival measurement using multiple static monopole antennas","volume":"15","author":"Malajner","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Song, C., Wang, J., and Yuan, G. (2016). Hidden naive bayes indoor fingerprinting localization based on best-discriminating ap selection. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5100189"},{"key":"ref_35","unstructured":"Wei, N., Gang, S., Xiaobing, L., and Luoning, G. (2006, January 11\u201314). An indoor location algorithm based on Taylor series expansion and maximum likelihood estimation. Proceedings of the 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2006, Helsinki, Finland."},{"key":"ref_36","first-page":"251","article-title":"KNN-FCM hybrid algorithm for indoor location in WLAN","volume":"Volume 2","author":"Sun","year":"2009","journal-title":"Proceedings of the 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS)"},{"key":"ref_37","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 2016, Alcala de Henares, Spain.","DOI":"10.1109\/IPIN.2016.7743694"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","unstructured":"Zhou, B., Yang, J., and Li, Q. (2019). Smartphone-based activity recognition for indoor localization using a convolutional neural network. Sensors, 19.","DOI":"10.3390\/s19030621"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1109\/JSEN.2019.2892590","article-title":"SWiBluX: Multi-sensor deep learning fingerprint for precise real-time indoor tracking","volume":"19","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ma, J., Li, X., Tao, X., and Lu, J. (2008, January 23\u201326). Cluster filtered KNN: A WLAN-based indoor positioning scheme. Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks, Newport Beach, CA, USA.","DOI":"10.1109\/WOWMOM.2008.4594840"},{"key":"ref_42","first-page":"392","article-title":"Cascade-connected ANN structures for indoor WLAN positioning","volume":"Volume 5788 LNCS","author":"Corchado","year":"2009","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"916","DOI":"10.4028\/www.scientific.net\/AMM.373-375.916","article-title":"Da Improved D\/TA and information fusion based on HMM indoor localization","volume":"373\u2013375","author":"Ru","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"21824","DOI":"10.3390\/s150921824","article-title":"An improved WiFi indoor positioning algorithm by weighted fusion","volume":"15","author":"Ma","year":"2015","journal-title":"Sensors"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"19551","DOI":"10.3390\/s141019551","article-title":"Impact of indoor environment on path loss in body area networks","volume":"14","author":"Hausman","year":"2014","journal-title":"Sensors"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, Z., Song, Y., McLoughlin, I., and Dai, L. (2016, January 20\u201325). Compact convolutional neural network transfer learning for small-scale image classification. Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472175"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hamidi-Rad, S., Lyons, K., and Goela, N. (2017, January 5\u20139). Infrastructure-less indoor localization using light fingerprints. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7953307"},{"key":"ref_48","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv Prepr."},{"key":"ref_49","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), Savannah, GA, USA."},{"key":"ref_50","unstructured":"Ge, X., and Qu, Z. (2016, January 26\u201328). Optimization WIFI indoor positioning KNN algorithm location-based fingerprint. Proceedings of the 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Khullar, R., and Dong, Z. (2017, January 7\u20138). Indoor localization framework with WiFi fingerprinting. Proceedings of the 2017 26th Wireless and Optical Communication Conference, WOCC 2017, Newark, NJ, USA.","DOI":"10.1109\/WOCC.2017.7928970"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Jedari, E., Wu, Z., Rashidzadeh, R., and Saif, M. (2015, January 13\u201316). Wi-Fi based indoor location positioning employing random forest classifier. Proceedings of the 2015 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2015, Banff, AB, Canada.","DOI":"10.1109\/IPIN.2015.7346754"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2981\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:32:05Z","timestamp":1760175125000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2981"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,24]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102981"],"URL":"https:\/\/doi.org\/10.3390\/s20102981","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,24]]}}}