{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:10:17Z","timestamp":1784646617600,"version":"3.55.0"},"reference-count":21,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"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":["61871054"],"award-info":[{"award-number":["61871054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Channel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. The CSI signals collected by different fingerprint points have a high degree of discrimination due to the influence of multi-path effects. This multi-path effect is reflected in the correlation between subcarriers and antennas. However, in mining such correlations, previous methods are difficult to aggregate non-adjacent features, resulting in insufficient multi-path information extraction. In addition, the existence of the multi-path effect makes the relationship between the original CSI signal and the distance not obvious, and it is easy to cause mismatching of long-distance points. Therefore, this paper proposes an indoor localization algorithm that combines the multi-head self-attention mechanism and effective CSI (MHSA-EC). This algorithm is used to solve the problem where it is difficult for traditional algorithms to effectively aggregate long-distance CSI features and mismatches of long-distance points. This paper verifies the stability and accuracy of MHSA-EC positioning through a large number of experiments. The average positioning error of MHSA-EC is 0.71 m in the comprehensive office and 0.64 m in the laboratory.<\/jats:p>","DOI":"10.3390\/e24050599","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T21:16:28Z","timestamp":1650921388000},"page":"599","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["MHSA-EC: An Indoor Localization Algorithm Fusing the Multi-Head Self-Attention Mechanism and Effective CSI"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6450-1969","authenticated-orcid":false,"given":"Wen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingjie","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongliang","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changyan","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7314","DOI":"10.1109\/TVT.2018.2833029","article-title":"Accurate WiFi localization by fusing a group of fingerprints via a global fusion profile","volume":"67","author":"Guo","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Abbas, M., Elhamshary, M., Rizk, H., Torki, M., and Youssef, M. (2019, January 11\u201315). WiDeep: WiFi-based accurate and robust indoor localization system using deep learning. Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications, Kyoto, Japan.","DOI":"10.1109\/PERCOM.2019.8767421"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Iqbal, Z., Luo, D., Henry, P., Kazemifar, S., Rozario, T., Yan, Y., Westover, K., Lu, W., Nguyen, D., and Long, T. (2018). Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0205392"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"04018034","DOI":"10.1061\/(ASCE)CP.1943-5487.0000778","article-title":"Technological viability assessment of Bluetooth low energy technology for indoor localization","volume":"32","author":"Topak","year":"2018","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_5","unstructured":"Jin, G.y., Lu, X.y., and Park, M.S. (2006, January 5\u20137). An indoor localization mechanism using active RFID tag. Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC\u201906), Taichung, Taiwan."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1142\/S2301385016400033","article-title":"Ultra-wideband-based localization for quadcopter navigation","volume":"4","author":"Guo","year":"2016","journal-title":"Unmanned Syst."},{"key":"ref_7","first-page":"706","article-title":"Ultra-wide-band based indoor positioning technologies","volume":"28","author":"Zhang","year":"2013","journal-title":"J. Data Acquis. Process."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zanca, G., Zorzi, F., Zanella, A., and Zorzi, M. (2008, January 1). Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks. Proceedings of the Workshop on Real-World Wireless Sensor Networks, Glasgow, UK.","DOI":"10.1145\/1435473.1435475"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.pmcj.2015.07.002","article-title":"CSI-MIMO: An efficient Wi-Fi fingerprinting using channel state information with MIMO","volume":"23","author":"Chapre","year":"2015","journal-title":"Pervasive Mob. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7990","DOI":"10.1109\/JSEN.2017.2762428","article-title":"Device-free presence detection and localization with SVM and CSI fingerprinting","volume":"17","author":"Zhou","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_11","unstructured":"Ramadan, M., Sark, V., Gutierrez, J., and Grass, E. (2018, January 14\u201316). NLOS identification for indoor localization using random forest algorithm. Proceedings of the WSA 2018, 22nd International ITG Workshop on Smart Antennas, VDE, Bochum, Germany."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xiu, C., Zhang, X., and Yang, D. (2018). WiFi indoor localization with CSI fingerprinting-based random forest. Sensors, 18.","DOI":"10.3390\/s18092869"},{"key":"ref_13","first-page":"763","article-title":"CSI-based fingerprinting for indoor localization: A deep learning approach","volume":"66","author":"Wang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"18066","DOI":"10.1109\/ACCESS.2017.2749516","article-title":"ConFi: Convolutional neural networks based indoor Wi-Fi localization using channel state information","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"108720","DOI":"10.1109\/ACCESS.2020.3000927","article-title":"LC-DNN: Local connection based deep neural network for indoor localization with CSI","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, W., Cheng, Q., Deng, Z., and Jia, M. (2021). C-GCN: A Flexible CSI Phase Feature Extraction Network for Error Suppression in Indoor Positioning. Entropy, 23.","DOI":"10.3390\/e23081004"},{"key":"ref_18","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, OUP Oxford."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.Y., Parmar, N., Shlens, J., Abbeel, P., and Vaswani, A. (2021, January 20\u201325). Bottleneck transformers for visual recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"ref_20","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tang, G., M\u00fcller, M., Rios, A., and Sennrich, R. (2018). Why self-attention? A targeted evaluation of neural machine translation architectures. arXiv.","DOI":"10.18653\/v1\/D18-1458"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/5\/599\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:00:53Z","timestamp":1760137253000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/5\/599"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":21,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["e24050599"],"URL":"https:\/\/doi.org\/10.3390\/e24050599","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,25]]}}}