{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:46:57Z","timestamp":1776275217178,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"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":["42001397"],"award-info":[{"award-number":["42001397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB0502102"],"award-info":[{"award-number":["2016YFB0502102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016836","name":"State Key Laboratory of Satellite Navigation System and Equipment Technology","doi-asserted-by":"publisher","award":["CEPNT-2018KF-03"],"award-info":[{"award-number":["CEPNT-2018KF-03"]}],"id":[{"id":"10.13039\/100016836","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A density-based spatial clustering of applications with noise (DBSCAN) and three distances (TD) integrated Wi-Fi positioning algorithm was proposed, aiming to enhance the positioning accuracy and stability of fingerprinting by the dynamic selection of signal-domain distance to obtain reliable nearest reference points (RPs). Two stages were included in this algorithm. One was the offline stage, where the offline fingerprint database was constructed and the other was the online positioning stage. Three distances (Euclidean distance, Manhattan distance, and cosine distance), DBSCAN, and high-resolution distance selection principle were combined to obtain more reliable nearest RPs and optimal signal-domain distance in the online stage. Fused distance, the fusion of position-domain and signal-domain distances, was applied for DBSCAN to generate the clustering results, considering both the spatial structure and signal strength of RPs. Based on the principle that the higher resolution the distance, the more clusters will be obtained, the high-resolution distance was used to compute positioning results. The weighted K-nearest neighbor (WKNN) considering signal-domain distance selection was used to estimate positions. Two scenarios were selected as test areas; a complex-layout room (Scenario A) for post-graduates and a typical large indoor environment (Scenario B) covering 3200 m2. In both Scenarios A and B, compared with support vector machine (SVM), Gaussian process regression (GPR) and rank algorithms, the improvement rates of positioning accuracy and stability of the proposed algorithm were up to 60.44 and 60.93%, respectively. Experimental results show that the proposed algorithm has a better positioning performance in complex and large indoor environments.<\/jats:p>","DOI":"10.3390\/rs14020297","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T22:03:13Z","timestamp":1641852193000},"page":"297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["DBSCAN and TD Integrated Wi-Fi Positioning Algorithm"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2964-7698","authenticated-orcid":false,"given":"Jingxue","family":"Bi","sequence":"first","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"},{"name":"State Key Laboratory of Geo-Information Engineering, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9743-2499","authenticated-orcid":false,"given":"Hongji","family":"Cao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunjia","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang Deqing Zhilu Navigation Technology Co., Ltd., Deqing County, Huzhou 313299, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"},{"name":"State Key Laboratory of Geo-Information Engineering, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.asr.2016.09.009","article-title":"Evaluation of GPS\/BDS indoor positioning performance and enhancement","volume":"59","author":"He","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e3997","DOI":"10.1002\/dac.3997","article-title":"A decision tree-based NLOS detection method for the UWB indoor location tracking accuracy improvement","volume":"32","author":"Ardiansyah","year":"2019","journal-title":"Int. J. Commun. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Poulose, A., and Han, D.S. (2020). UWB indoor localization using deep learning LSTM networks. Appl. Sci., 10.","DOI":"10.3390\/app10186290"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s41651-020-00059-2","article-title":"A UWB-Based Indoor Positioning System Employing Neural Networks","volume":"4","author":"Li","year":"2020","journal-title":"J. Geovis. Spat. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1007\/s11277-012-0777-1","article-title":"Bayesian Fusion for Indoor Positioning Using Bluetooth Fingerprints","volume":"70","author":"Chen","year":"2013","journal-title":"Wirel. Pers. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4115","DOI":"10.1007\/s11277-017-4371-4","article-title":"Bluetooth Indoor Positioning Based on RSSI and Kalman Filter","volume":"96","author":"Zhou","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Poulose, A., Kim, J., and Han, D.S. (2019). A sensor fusion framework for indoor localization using smartphone sensors and Wi-Fi RSSI measurements. Appl. Sci., 9.","DOI":"10.3390\/app9204379"},{"key":"ref_8","unstructured":"Beomju, S., Jung Ho, L., Taikjin, L., and Hyung Seok, K. (2012, January 24\u201326). Enhanced Weighted K-Nearest Neighbor Algorithm for Indoor WI-FI Positioning Systems. Proceedings of the 8th International Conference on Computing Technology and Information Management (NCM and ICNIT), Seoul, Korea."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/LSP.2016.2519607","article-title":"An Improved K-Nearest-Neighbor Indoor Localization Method Based on Spearman Distance","volume":"23","author":"Xie","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"4654","DOI":"10.1109\/JSEN.2018.2828044","article-title":"An Indoor Positioning System Based on the Dual-Channel Passive RFID Technology","volume":"18","author":"Yao","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.measurement.2018.12.071","article-title":"A novel 3D position measurement and structure prediction method for RFID tag group based on deep belief network","volume":"136","author":"Zhuang","year":"2019","journal-title":"Measurement"},{"key":"ref_13","unstructured":"Liu, Z., Chen, R., Ye, F., Guo, G., Li, Z., and Qian, L. (2020). Improved TOA Estimation Method for Acoustic Ranging in a Reverberant Environment. IEEE Sens. J., 1\u20138."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"13716","DOI":"10.1109\/JSEN.2020.3006930","article-title":"Simultaneous excitation systems for ultrasonic indoor positioning","volume":"20","author":"Khyam","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7472","DOI":"10.1109\/JSEN.2016.2600099","article-title":"Basmag: An Optimized HMM-Based Localization System Using Backward Sequences Matching Algorithm Exploiting Geomagnetic Information","volume":"16","author":"Ma","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_16","first-page":"5989678","article-title":"An Improved Particle Filter Algorithm for Geomagnetic Indoor Positioning","volume":"2018","author":"Gao","year":"2018","journal-title":"J. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fujii, K., Yonezawa, R., Sakamoto, Y., Schmitz, A., and Sugano, S. (2016, January 4\u20137). A Combined Approach of Doppler and Carrier-Based Hyperbolic Positioning with a Multi-Channel GPS-Pseudolite for Indoor Localization of Robots. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Madrid, Spain.","DOI":"10.1109\/IPIN.2016.7743668"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10291-019-0870-y","article-title":"Performance analysis of indoor pseudolite positioning based on the unscented Kalman filter","volume":"23","author":"Li","year":"2019","journal-title":"GPS Solut."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xiao, A., Ruizhi, C., Deren, L., Yujin, C., and Dewen, W. (2018). An Indoor Positioning System Based on Static Objects in Large Indoor Scenes by Using Smartphone Cameras. Sensors, 18.","DOI":"10.3390\/s18072229"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yao, G., Yilmaz, A., Zhang, L., Meng, F., Ai, H., and Jin, F. (2021). Matching Large Baseline Oblique Stereo Images Using an End-To-End Convolutional Neural Network. Remote Sens., 13.","DOI":"10.3390\/rs13020274"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14401","DOI":"10.1109\/JSEN.2020.2998815","article-title":"UWB\/INS integrated pedestrian positioning for robust indoor environments","volume":"20","author":"Zhang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11165","DOI":"10.1109\/ACCESS.2019.2891942","article-title":"An indoor position-estimation algorithm using smartphone IMU sensor data","volume":"7","author":"Poulose","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Poulose, A., and Han, D.S. (2019). Hybrid Indoor Localization Using IMU Sensors and Smartphone Camera. Sensors, 19.","DOI":"10.3390\/s19235084"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Li, C., Zhen, J., Chang, K., Xu, A., Zhu, H., and Wu, J. (2021). An Indoor Positioning and Tracking Algorithm Based on Angle-of-Arrival Using a Dual-Channel Array Antenna. Remote Sens., 13.","DOI":"10.3390\/rs13214301"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Poulose, A., and Han, D.S. (2020, January 19\u201321). Performance Analysis of Fingerprint Matching Algorithms for Indoor Localization. Proceedings of the International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan.","DOI":"10.1109\/ICAIIC48513.2020.9065220"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1049\/iet-com.2019.1059","article-title":"Survey on WiFi-based indoor positioning techniques","volume":"14","author":"Liu","year":"2020","journal-title":"IET Commun."},{"key":"ref_28","unstructured":"Feng, X., Nguyen, K.A., and Luo, Z. (2021). A survey of deep learning approaches for WiFi-based indoor positioning. J. Inf. Telecommun., 1\u201354."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mendoza-Silva, G.M., Torres-Sospedra, J., and Huerta, J. (2019). A meta-review of indoor positioning systems. Sensors, 19.","DOI":"10.3390\/s19204507"},{"key":"ref_30","unstructured":"Bahl, P., and Padmanabhan, V.N. (2000, January 6). RADAR: An In-Building RF-Based User Location and Tracking System. Proceedings of the IEEE INFOCOM, Tel Aviv, Israel."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.icte.2018.04.004","article-title":"Adaptive K-nearest neighbour algorithm for WiFi fingerprint positioning","volume":"4","author":"Oh","year":"2018","journal-title":"ICT Express"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xie, H., Gu, T., Tao, X., Ye, H., and Lv, J. (2014, January 13\u201317). MaLoc: A Practical Magnetic Fingerprinting Approach to Indoor Localization Using Smartphones. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA.","DOI":"10.1145\/2632048.2632057"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1186\/1687-1499-2013-272","article-title":"Fingerprint indoor positioning algorithm based on affinity propagation clustering","volume":"2013","author":"Tian","year":"2013","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1109\/TMC.2012.124","article-title":"Enhanced Least-Squares Positioning Algorithm for Indoor Positioning","volume":"12","author":"Sharp","year":"2013","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, G., and Tseng, P. (2018, January 5\u20138). A Deep Neural Network-Based Indoor Positioning Method using Channel State Information. Proceedings of the International Conference on Computing, Networking and Communications (ICNC), Maui, HI, USA.","DOI":"10.1109\/ICCNC.2018.8390298"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111813","DOI":"10.1109\/ACCESS.2019.2931992","article-title":"A Particle Filter Based Reference Fingerprinting Map Recalibration Method","volume":"7","author":"Chu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Minaev, G., Visa, A., and Piche, R. (2017, January 18\u201321). Comprehensive survey of similarity measures for ranked based location fingerprinting algorithm. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.","DOI":"10.1109\/IPIN.2017.8115922"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9263","DOI":"10.1016\/j.eswa.2015.08.013","article-title":"Comprehensive Analysis of Distance and Similarity Measures for Wi-Fi Fingerprinting Indoor Positioning Systems","volume":"42","author":"Montoliu","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lohan, E.S., Torres-Sospedra, J., Lepp\u00e4koski, H., Richter, P., Peng, Z., and Huerta, J. (2017). Wi-Fi Crowdsourced Fingerprinting Dataset for Indoor Positioning. Data, 2.","DOI":"10.3390\/data2040032"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1049\/el.2018.0621","article-title":"Fingerprint Liveness Map Construction Using Convolutional Neural Network","volume":"54","author":"Jung","year":"2018","journal-title":"Electron. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/JIOT.2015.2481932","article-title":"Gaussian Process Assisted Fingerprinting Localization","volume":"3","author":"Yiu","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"215777","DOI":"10.1109\/ACCESS.2020.3041773","article-title":"WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"110698","DOI":"10.1109\/ACCESS.2019.2933921","article-title":"A novel convolutional neural network based indoor localization framework with WiFi fingerprinting","volume":"7","author":"Song","year":"2019","journal-title":"IEEE Access"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8250","DOI":"10.1109\/TIE.2018.2803720","article-title":"BFVP: A Probabilistic UHF RFID Tag Localization Algorithm Using Bayesian Filter and a Variable Power RFID Model","volume":"65","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_46","unstructured":"Li, B., Dempster, A.G., Barnes, J., Rizos, C., and Li, D. (2005, January 8\u201310). Probabilistic Algorithm to Support the Fingerprinting Method for CDMA Location. Proceedings of the International Symposium on GPS\/GNSS, Hong Kong, China."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sangeetha, S., and Radha, N. (2013, January 4\u20135). A New Framework for IRIS and Fingerprint Recognition Using SVM Classification and Extreme Learning Machine Based on Score Level Fusion. Proceedings of the 7th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India.","DOI":"10.1109\/ISCO.2013.6481145"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"263","DOI":"10.3233\/AIS-170421","article-title":"A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL\u2013ETRI competition","volume":"9","author":"Moreira","year":"2017","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"52110","DOI":"10.1109\/ACCESS.2019.2911601","article-title":"Harvesting Indoor Positioning Accuracy by Exploring Multiple Features from Received Signal Strength Vector","volume":"7","author":"Ali","year":"2019","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"10896","DOI":"10.1109\/TVT.2018.2870160","article-title":"Augmentation of fingerprints for indoor WiFi localization based on Gaussian process regression","volume":"67","author":"Sun","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/0031-3203(78)90018-3","article-title":"Agglomerative clustering using the concept of mutual nearest neighbourhood","volume":"10","author":"Gowda","year":"1978","journal-title":"Pattern Recognit."},{"key":"ref_52","unstructured":"Chen, G., Liu, Q., Wei, Y., and Yu, Q. (2016, January 14\u201317). An Efficient Indoor Location System in WLAN Based on Database Partition and Euclidean Distance-Weighted Pearson Correlation Coefficient. Proceedings of the 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China."},{"key":"ref_53","unstructured":"Retscher, G., and Joksch, J. (2016, January 14\u201316). Comparison of Different Vector Distance Measure Calculation Variants for Indoor Location Fingerprinting. Proceedings of the 13th International Conference on Location-Based Services, Vienna, Austria."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.apm.2018.06.031","article-title":"Indoor positioning system based on BLE location fingerprinting with classification approach","volume":"62","author":"Pu","year":"2018","journal-title":"Appl. Math. Model."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1007\/s11277-017-4295-z","article-title":"An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Positioning","volume":"96","author":"Li","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Marques, N., Meneses, F., and Moreira, A. (2012, January 13\u201315). Combining Similarity Functions and Majority Rules for Multi-Building, Multi-Floor, WiFi Positioning. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sydney, Australia.","DOI":"10.1109\/IPIN.2012.6418937"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Farshad, A., Jiwei, L., Marina, M.K., and Garcia, F.J. (2013, January 28\u201331). A Microscopic Look at WiFi Fingerprinting for Indoor Mobile Phone Localization in Diverse Environments. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Montbeliard, France.","DOI":"10.1109\/IPIN.2013.6817920"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1550147718785885","DOI":"10.1177\/1550147718785885","article-title":"A novel method of adaptive weighted K-nearest neighbor fingerprint indoor positioning considering user\u2019s orientation","volume":"14","author":"Bi","year":"2018","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhou, H., and Van, N.N. (2014, January 10\u201311). Indoor Fingerprint Localization Based on Fuzzy C-Means Clustering. Proceedings of the Sixth International Conference on Measuring Technology and Mechatronics Automation, Zhangjiajie, China.","DOI":"10.1109\/ICMTMA.2014.83"},{"key":"ref_60","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/297\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:14:43Z","timestamp":1760364883000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,10]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14020297"],"URL":"https:\/\/doi.org\/10.3390\/rs14020297","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,10]]}}}