{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:08:46Z","timestamp":1760058526099,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["62266035","62376114"],"award-info":[{"award-number":["62266035","62376114"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Despite the widespread adoption of indoor positioning technology, the existing solutions still face significant challenges. On one hand, Wi-Fi-based positioning struggles to balance accuracy and efficiency in complex indoor environments and architectural layouts formed by pre-existing access points (APs). On the other hand, vision-based methods, while offering high-precision potential, are hindered by prohibitive costs associated with binocular camera systems required for depth image acquisition, limiting their large-scale deployment. Additionally, channel state information (CSI), containing multi-subcarrier data, maintains amplitude symmetry in ideal free-space conditions but becomes susceptible to periodic positioning errors in real environments due to multipath interference. Meanwhile, image-based positioning often suffers from spatial ambiguity in texture-repeated areas. To address these challenges, we propose a novel hybrid indoor positioning method that integrates multi-granularity and multi-modal features. By fusing CSI data with visual information, the system leverages spatial consistency constraints from images to mitigate CSI error fluctuations while utilizing CSI\u2019s global stability to correct local ambiguities in image-based positioning. In the initial coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, CSI features from Wi-Fi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. The experimental results show that our proposed method synergistically combines the strengths of Wi-Fi fingerprints and visual positioning, resulting in a substantial enhancement in positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 m for 45% of positioning points and 0.8 m for 67% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology.<\/jats:p>","DOI":"10.3390\/sym17040597","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T08:11:39Z","timestamp":1744704699000},"page":"597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Granularity and Multi-Modal Feature Fusion for Indoor Positioning"],"prefix":"10.3390","volume":"17","author":[{"given":"Lijuan","family":"Ye","sequence":"first","affiliation":[{"name":"College of Mathematics and Statistic, Qinghai Minzu University, Xining 810007, China"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Engineering, Qinghai Minzu University, Xining 810007, China"},{"name":"National Demonstration Center for Experimental Communication Engineering Education, Qinghai Minzu University, Xining 810007, China"}]},{"given":"Shenglei","family":"Pei","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Engineering, Qinghai Minzu University, Xining 810007, China"},{"name":"National Demonstration Center for Experimental Communication Engineering Education, Qinghai Minzu University, Xining 810007, China"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin 300350, China"}]},{"given":"Hong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou 363000, China"}]},{"given":"Shi","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Engineering, Qinghai Minzu University, Xining 810007, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, C., Shi, Z., and Wu, F. (2017). Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine. Symmetry, 9.","DOI":"10.3390\/sym9030030"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kang, J., Seo, J., and Won, Y. (2018). Ephemeral ID Beacon-Based Improved Indoor Positioning System. Symmetry, 10.","DOI":"10.3390\/sym10110622"},{"key":"ref_3","first-page":"135","article-title":"Sustainable development of animal husbandry in China","volume":"34","author":"Chen","year":"2019","journal-title":"Bull. Chin. Acad. Sci. (Chin. Version)"},{"key":"ref_4","first-page":"1","article-title":"Development history and trend of beidou satellite navigation system","volume":"10","author":"Yang","year":"2022","journal-title":"J. Navig. Position."},{"key":"ref_5","unstructured":"Tu, W., and Guo, C. (2021, January 26\u201328). Review of indoor positioning methods based on machine learning. Proceedings of the 12th China Satellite Navigation Annual Conference, Nanchang, China."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lu, H., Liu, S., and Hwang, S.H. (2025). Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning. Electronics, 14.","DOI":"10.3390\/electronics14061136"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.patcog.2017.09.013","article-title":"A survey on visual-based localization: On the benefit of heterogeneous data","volume":"74","author":"Piasco","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_8","first-page":"13","article-title":"Fingerprint positioning method of CSI based on PCA-SMO","volume":"46","author":"Meng","year":"2021","journal-title":"GNSS World China"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lee, M., and Choi, S. (2021). Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories. Sensors, 21.","DOI":"10.3390\/s21175776"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Che, R., and Chen, H. (2023). Channel State Information Based Indoor Fingerprinting Localization. Sensors, 23.","DOI":"10.3390\/s23135830"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4283857","DOI":"10.1155\/2019\/4283857","article-title":"Combination of DNN and Improved KNN for indoor location fingerprinting","volume":"2019","author":"Dai","year":"2019","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_12","first-page":"62","article-title":"CSI indoor positioning based on Kmeans clustering","volume":"42","author":"Tian","year":"2016","journal-title":"Appl. Electron. Tech."},{"key":"ref_13","first-page":"853","article-title":"An indoor positioning method based on CSI and SVM regression","volume":"43","author":"Dang","year":"2021","journal-title":"Comput. Eng. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14909","DOI":"10.1007\/s00521-020-04847-1","article-title":"DFPhaseFL: A robust device-free passive fingerprinting wireless localization system using CSI phase information","volume":"32","author":"Rao","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3203","DOI":"10.1007\/s13042-021-01279-8","article-title":"Localizing pedestrians in indoor environments using magnetic field data with term frequency paradigm and deep neural networks","volume":"12","author":"Ashraf","year":"2021","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, D., Zhang, D., Xu, C., Wang, Y., and Wang, H. (2016, January 12\u201316). WiDir: Walking direction estimation using wireless signals. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg Germany.","DOI":"10.1145\/2971648.2971658"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s13042-022-01559-x","article-title":"Device-free indoor localization based on sparse coding with nonconvex regularization and adaptive relaxation localization criteria","volume":"14","author":"Zhang","year":"2023","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_18","first-page":"1826","article-title":"WLAN indoor positioning algorithm based on manifold interpolation database construction","volume":"39","author":"Zhou","year":"2017","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bai, Y., Jia, W., Zhang, H., Mao, Z., and Sun, M. (2014, January 19\u201323). Landmark-based indoor positioning for visually impaired individuals. Proceedings of the 2014 12th International Conference on Signal Processing (ICSP), Hangzhou, China.","DOI":"10.1109\/ICOSP.2014.7015087"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, J., Chen, L., and Liang, W. (2010, January 14\u201318). Monocular vision based robot self-localization. Proceedings of the 2010 IEEE International Conference on Robotics and Biomimetics, Tianjin, China.","DOI":"10.1109\/ROBIO.2010.5723497"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1007\/s13042-020-01078-7","article-title":"Pothole detection using location-aware convolutional neural networks","volume":"11","author":"Chen","year":"2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Werner, M., Kessel, M., and Marouane, C. (2011, January 21\u201323). Indoor positioning using smartphone camera. Proceedings of the 2011 International Conference on Indoor Positioning and Indoor Navigation, Guimaraes, Portugal.","DOI":"10.1109\/IPIN.2011.6071954"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dryanovski, I., Valenti, R., and Xiao, J. (2013, January 6\u201310). Fast visual odometry and mapping from RGB-D data. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630889"},{"key":"ref_24","first-page":"93","article-title":"Vision-based localization method for indoor mobile robots based on line detection","volume":"11","author":"Zhou","year":"2016","journal-title":"J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_25","unstructured":"Buyval, A., Mustafin, R., Gavrilenkov, M., Gabdullin, A., and Shimchik, I. Visual localization for copter based on 3D model of environment with CNN segmentation. Proceedings of the Information Science and Cloud Computing (ISCC 2017)."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"17920","DOI":"10.1007\/s10489-022-04362-x","article-title":"OCR-RTPS: An OCR-based real-time positioning system for the valet parking","volume":"53","author":"Wu","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, K., Huang, Y., and Song, X. (2023, January 25\u201326). Convolutional transformer network: Future pedestrian location in first-person videos using depth map and 3D pose. Proceedings of the 22nd Asia Simulation Conference, Langkawi, Malaysia.","DOI":"10.1007\/978-981-99-7240-1_4"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s44176-023-00020-9","article-title":"Location optimization of on-campus bicycle-sharing electronic fences","volume":"2","author":"Fu","year":"2023","journal-title":"Manag. Syst. Eng."},{"key":"ref_29","first-page":"1237","article-title":"DSHFS: A new hybrid approach that detects structures with their spatial location from large volume satellite images using CNN, GeoServer and TileCache","volume":"36","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_30","first-page":"165","article-title":"Augmented reality (AR) and spatial cognition: Effects of holographic grids on distance estimation and location memory in a 3D indoor scenario","volume":"88","author":"Keil","year":"2020","journal-title":"PFG Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_31","first-page":"89","article-title":"Minimize the routing overhead through 3D cone shaped location-aided routing protocol for FANETs","volume":"13","author":"Kumar","year":"2021","journal-title":"Int. J. Inf. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s41074-020-00066-8","article-title":"3D human pose estimation model using location-maps for distorted and disconnected images by a wearable omnidirectional camera","volume":"12","author":"Miura","year":"2020","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s13638-022-02102-w","article-title":"3D target location based on RFID polarization phase model","volume":"2022","author":"Shi","year":"2022","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, H., Mei, T., Li, H., and Luo, J. (2015). Vision-based fine-grained location estimation. Multimodal Location Estimation of Videos and Images, Springer.","DOI":"10.1007\/978-3-319-09861-6_4"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1007\/s11119-022-09974-4","article-title":"Three-dimensional location methods for the vision system of strawberry-harvesting robots: Development and comparison","volume":"24","author":"Ge","year":"2023","journal-title":"Precis. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1109\/JIOT.2019.2948605","article-title":"Enhancing camera-based multi-modal indoor localization with device-free movement measurement using WiFi","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dong, J., Xiao, Y., Noreikis, M., Ou, Z., and Yl-Jski, A. (2015, January 1\u20134). iMoon: Using smartphones for image-based indoor navigation. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, Seoul, Republic of Korea.","DOI":"10.1145\/2809695.2809722"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chang, Y., Chen, J., Franklin, T., Zhang, L., Ruci, A., Tang, H., and Zhu, Z. (2020, January 11\u201313). Multimodal information integration for indoor navigation using a smartphone. Proceedings of the 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, NV, USA.","DOI":"10.1109\/IRI49571.2020.00017"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9261","DOI":"10.1007\/s00521-018-3945-8","article-title":"MSDFL: A robust minimal hardware low-cost device-free WLAN localization system","volume":"31","author":"Rao","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Agah, N., Evans, B., Meng, X., and Xu, H. (2023, January 1\u201316). A local machine learning approach for fingerprint-based indoor localization. Proceedings of the SoutheastCon 2023, Orlando, FL, USA.","DOI":"10.1109\/SoutheastCon51012.2023.10115169"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/597\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:14:56Z","timestamp":1760030096000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/4\/597"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,15]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["sym17040597"],"URL":"https:\/\/doi.org\/10.3390\/sym17040597","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,4,15]]}}}