{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:38:46Z","timestamp":1768415926119,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T00:00:00Z","timestamp":1621987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1I1A3A01063290"],"award-info":[{"award-number":["2019R1I1A3A01063290"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.<\/jats:p>","DOI":"10.3390\/s21113701","type":"journal-article","created":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T21:56:44Z","timestamp":1622066204000},"page":"3701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8198-0439","authenticated-orcid":false,"given":"Ju-Hyeon","family":"Seong","sequence":"first","affiliation":[{"name":"Department of Liberal Education, Korea Maritime and Ocean University, Busan 49112, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soo-Hwan","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Interdisciplinary Major of Maritime AI Convergence, Korea Maritime and Ocean University, Busan 49112, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Won-Yeol","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Interdisciplinary Major of Maritime AI Convergence, Korea Maritime and Ocean University, Busan 49112, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong-Hoan","family":"Seo","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Information Engineering, Interdisciplinary Major of Maritime AI Convergence, Korea Maritime and Ocean University, Busan 49112, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez, A.R., and Seco, F. (2021). Improving the Accuracy of Decawave\u2019s UWB MDEK1001 Location System by Gaining Access to Multiple Ranges. Sensors, 21.","DOI":"10.3390\/s21051787"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Frank\u00f3, A., Vida, G., and Varga, P. (2020). Reliable identification schemes for asset and production tracking in industry 4.0. Sensors, 20.","DOI":"10.3390\/s20133709"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10291-021-01101-6","article-title":"Probabilistic approach to detect and correct GNSS NLOS signals using an augmented state vector in the extended Kalman filter","volume":"25","author":"Jiang","year":"2021","journal-title":"GPS Solut."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"51536","DOI":"10.1109\/ACCESS.2021.3064383","article-title":"Navigation in GPS Spoofed Environment Using M-Best Positioning Algorithm and Data Association","volume":"9","author":"Pardhasaradhi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xiang, C., Zhang, S., Xu, S., and Alexandropoulos, G.C. (2021). Self-Calibrating Indoor Localization with Crowdsourcing Fingerprints and Transfer Learning. arXiv Prepr.","DOI":"10.1109\/ICC42927.2021.9500623"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1080\/17489725.2020.1817582","article-title":"Deep learning methods for fingerprint-based indoor positioning: A review","volume":"14","author":"Alhomayani","year":"2020","journal-title":"J. Locat. Based Serv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1007\/s11276-018-1700-9","article-title":"Wi-Fi fingerprint using radio map model based on MDLP and Euclidean distance based on the Chi squared test","volume":"25","author":"Seong","year":"2019","journal-title":"Wirel. Netw."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ssekidde, P., Steven Eyobu, O., Han, D.S., and Oyana, T.J. (2021). Augmented CWT Features for Deep Learning-Based Indoor Localization Using WiFi RSSI Data. Appl. Sci., 11.","DOI":"10.3390\/app11041806"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sun, H., Zhu, X., Liu, Y., and Liu, W. (2020). Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method. Sensors, 20.","DOI":"10.3390\/s20102981"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, J., and Park, J.G. (2020, January 20\u201323). An enhanced indoor ranging method using CSI measurements with Extended Kalman filter. Proceedings of the 2020 IEEE\/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA.","DOI":"10.1109\/PLANS46316.2020.9109802"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dang, X., Tang, X., Hao, Z., and Ren, J. (2020). Discrete Hopfield neural network based indoor Wi-Fi localization using CSI. EURASIP J. Wirel. Commun. Netw., 76.","DOI":"10.1186\/s13638-020-01692-7"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"136858","DOI":"10.1109\/ACCESS.2020.3012342","article-title":"A low cost indoor positioning system using Bluetooth low energy","volume":"8","author":"Bai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.comcom.2020.04.041","article-title":"Decentralized adaptive indoor positioning protocol using Bluetooth Low Energy","volume":"159","author":"Ho","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Seong, J.H., Lee, S.H., Yoon, K.K., and Seo, D.H. (2019). Ellipse coefficient map-based geomagnetic fingerprint considering azimuth angles. Symmetry, 11.","DOI":"10.3390\/sym11050708"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6509","DOI":"10.1007\/s11277-017-4852-5","article-title":"Advanced indoor positioning using zigbee wireless technology","volume":"97","author":"Uradzinski","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1504\/IJES.2020.109963","article-title":"An improved method for indoor positioning based on ZigBee technique","volume":"13","author":"Zhen","year":"2020","journal-title":"Int. J. Embed. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, M., Qiao, Y., Zhang, B., and Yang, H. (2020). Efficient marginalized particle smoother for indoor CSS\u2013TOF localization with non-Gaussian errors. Remote Sens., 12.","DOI":"10.3390\/rs12223838"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1109\/TMC.2020.2973159","article-title":"Revitalizing Ultrasonic Positioning Systems for Ultrasound-Incapable Smart Devices","volume":"20","author":"An","year":"2020","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.1109\/JIOT.2020.2965115","article-title":"Kalman-filter-based integration of IMU and UWB for high-accuracy indoor positioning and navigation","volume":"7","author":"Feng","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"139387","DOI":"10.1109\/ACCESS.2020.3012717","article-title":"Toward elderly care: A phase-difference-of-arrival assisted ultra-wideband positioning method in smart home","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1109\/TITS.2016.2516822","article-title":"Positioning techniques in indoor environments based on stochastic modeling of UWB round-trip-time measurements","volume":"17","author":"Moschitta","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Martinelli, A., Jayousi, S., Caputo, S., and Mucchi, L. (October, January 30). UWB positioning for industrial applications: The galvanic plating case study. Proceedings of the 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa, Italy.","DOI":"10.1109\/IPIN.2019.8911746"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s10776-019-00470-7","article-title":"Ultra-wide Band Positioning in Sport: How the Relative Height Between the Transmitting and the Receiving Antenna Affects the System Performance","volume":"27","author":"Martinelli","year":"2020","journal-title":"Int. J. Wirel. Inf. Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"49671","DOI":"10.1109\/ACCESS.2020.2979186","article-title":"Indoor positioning tightly coupled Wi-Fi FTM ranging and PDR based on the extended Kalman filter for smartphones","volume":"8","author":"Sun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11006","DOI":"10.1109\/JIOT.2020.2992069","article-title":"Accurate indoor positioning using temporal-spatial constraints based on Wi-Fi fine time measurements","volume":"7","author":"Shao","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fang, X., and Chen, L. (2020). An optimal multi-channel trilateration localization algorithm by radio-multipath multi-objective evolution in RSS-ranging-based wireless sensor networks. Sensors, 20.","DOI":"10.3390\/s20061798"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8164","DOI":"10.1109\/JSEN.2020.2980966","article-title":"A novel trilateration algorithm for RSSI-based indoor localization","volume":"20","author":"Yang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shi, Y., Shi, W., Liu, X., and Xiao, X. (2020). An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning. Sensors, 20.","DOI":"10.3390\/s20154244"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Bi, J., Xu, S., Si, M., and Qi, H. (2020). Indoor Positioning Method Using WiFi RTT Based on LOS Identification and Range Calibration. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9110627"},{"key":"ref_31","unstructured":"Ma, C., Wu, B., Poslad, S., and Selviah, D.R. (2020). Wi-Fi RTT Ranging Performance Characterization and Positioning System Design. IEEE Trans. Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4515","DOI":"10.3390\/s20164515","article-title":"Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios","volume":"20","author":"Markus","year":"2020","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Horn, B.K. (2020). Doubling the Accuracy of Indoor Positioning: Frequency Diversity. Sensors, 20.","DOI":"10.3390\/s20051489"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gentner, C., Ulmschneider, M., Kuehner, I., and Dammann, A. (2020, January 20\u201323). WiFi-RTT Indoor Positioning. Proceedings of the 2020 IEEE\/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA.","DOI":"10.1109\/PLANS46316.2020.9110232"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, L., Yu, B., Li, H., Zhang, H., Li, S., Zhu, R., and Li, Y. (2020). HPIPS: A high-precision indoor pedestrian positioning system fusing WiFi-RTT, MEMS, and map information. Sensors, 20.","DOI":"10.3390\/s20236795"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1898","DOI":"10.1109\/JIOT.2019.2956986","article-title":"Selective unsupervised learning-based Wi-Fi fingerprint system using autoencoder and GAN","volume":"7","author":"Seong","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"33256","DOI":"10.1109\/ACCESS.2019.2903487","article-title":"Deep learning-based indoor localization using received signal strength and channel state information","volume":"7","author":"Hsieh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_38","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":"2018","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/LCOMM.2020.3039251","article-title":"LOS\/NLOS identification for indoor UWB positioning based on Morlet wavelet transform and convolutional neural networks","volume":"25","author":"Cui","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T.A., Lee, H.G., Jeong, E.R., Lee, H.L., and Joung, J. (2020). Deep learning-based localization for UWB systems. Electronics, 9.","DOI":"10.3390\/electronics9101712"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1109\/TVT.2018.2805189","article-title":"Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries","volume":"67","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wu, L., Chen, C.H., and Zhang, Q. (2019). A mobile positioning method based on deep learning techniques. Electronics, 8.","DOI":"10.3390\/electronics8010059"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.1109\/JIOT.2020.3024845","article-title":"DFOPS: Deep learning-based fingerprinting outdoor positioning scheme in hybrid networks","volume":"8","author":"Tarekegn","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sun, H., Zhu, X., Liu, Y., and Liu, W. (2020). WiFi based fingerprinting positioning based on Seq2seq model. Sensors, 20.","DOI":"10.3390\/s20133767"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"10639","DOI":"10.1109\/JIOT.2019.2940368","article-title":"Recurrent neural networks for accurate RSSI indoor localization","volume":"6","author":"Hoang","year":"2019","journal-title":"IEEE Internet Things J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3701\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:08:16Z","timestamp":1760162896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/11\/3701"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,26]]},"references-count":45,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21113701"],"URL":"https:\/\/doi.org\/10.3390\/s21113701","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,26]]}}}