{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:55:29Z","timestamp":1764240929018,"version":"3.46.0"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In Ultra-Wide Band (UWB) positioning, wireless signals are subject to non-line-of-sight (NLOS) propagation due to obstruction by obstacles, which leads to ranging and positioning estimation errors. How to accurately and efficiently identify line-of-sight (LOS) and NLOS propagation paths is a key research task in UWB positioning systems. By effectively integrating the characteristics of global channel impulse response (CIR) sequence features and statistical time-domain features, a dual-branch feature fusion Transformer (DBFF-Transformer) is proposed for NLOS path identification. Firstly, the original CIR sequence data is processed using the Transformer to learn the global feature relationships within the data. Secondly, four key time-domain features are extracted from the CIR sequence: the first-path energy ratio, the root-mean-square time delay spread, the kurtosis and the phase difference. Finally, by integrating the sequence features and the time-domain features, the two features\u2019 branches are fused through a fully connected network. The proposed method is evaluated in two typical indoor scenarios from the latest open-source datasets of the eWINE project. The ablation experiment proves that the fusion of the sequence features and time-domain features of the CIR sequence can effectively improve NLOS identification accuracy. The identification accuracy in the two experimental scenarios is 95.9% and 95.7%, with F1 scores of 97.2% and 97.1% and Recall of 97.4% and 96.4%, respectively. The comparative analysis of the DBFF-Transformer with the state-of-the-art baseline models demonstrates superior accuracy and robustness, which can provide a novel solution for NLOS identification in UWB indoor positioning.<\/jats:p>","DOI":"10.3390\/info16121033","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:25:16Z","timestamp":1764239116000},"page":"1033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Non-Line-of-Sight Identification Method for Ultra-Wide Band Based on Dual-Branch Feature Fusion Transformer"],"prefix":"10.3390","volume":"16","author":[{"given":"Guangyong","family":"Xi","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Shuaiyang","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1025-5974","authenticated-orcid":false,"given":"Dongyao","family":"Zou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, F., and Ma, J. (2023). Indoor location technology with high accuracy using simple visual tags. Sensors, 23.","DOI":"10.3390\/s23031597"},{"key":"ref_2","first-page":"6","article-title":"Indoor positioning system: A review","volume":"13","author":"Syazwani","year":"2022","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/10589759.2023.2253493","article-title":"RSSI-based location fingerprint method for RFID indoor positioning: A review","volume":"39","author":"Wei","year":"2024","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2205513","DOI":"10.1155\/2024\/2205513","article-title":"Improved Pedestrian Location Method for the Indoor Environment Based on MIMU and sEMG Sensors","volume":"2024","author":"Ling","year":"2024","journal-title":"J. Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1587\/transinf.2022DLP0073","article-title":"An improved BPNN method based on probability density for indoor location","volume":"106","author":"Fei","year":"2023","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e12620","DOI":"10.1111\/coin.12620","article-title":"Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement","volume":"40","author":"Kim","year":"2024","journal-title":"Comput. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"26320","DOI":"10.1109\/JSEN.2024.3420727","article-title":"MLA-MFL: A Smartphone Indoor Localization Method for Fusing Multi-source Sensors under Multiple Scene Conditions","volume":"24","author":"Jianhua","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"To\u0161i\u0107, A., Hrovatin, N., and Vi\u010di\u010d, J. (2022). A WSN framework for privacy aware indoor location. Appl. Sci., 12.","DOI":"10.3390\/app12063204"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, H., Wang, Y., Xu, S., Bi, J., Jia, H., and Seow, C. (2024). Ultra-wideband ranging error mitigation with novel channel impulse response feature parameters and two-step non-line-of-sight identification. Sensors, 24.","DOI":"10.3390\/s24051703"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"115904","DOI":"10.1016\/j.sna.2024.115904","article-title":"UWB indoor localization method based on neural network multi-classification for NLOS distance correction","volume":"379","author":"Tu","year":"2024","journal-title":"Sens. Actuators A Phys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1007\/s11277-024-11300-2","article-title":"A Survey on Scalable Wireless Indoor Localization: Techniques, Approaches and Directions","volume":"136","author":"Abraha","year":"2024","journal-title":"Wirel. Pers. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105397","DOI":"10.1016\/j.dsp.2025.105397","article-title":"Localization through mitigating and compensating UWB NLOS ranging error with neural network","volume":"166","author":"Shalihan","year":"2025","journal-title":"Digit. Signal Process."},{"key":"ref_13","first-page":"2508218","article-title":"UWB localization in a smart factory: Augmentation methods and experimental assessment","volume":"70","author":"Barbieri","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1145\/3663473","article-title":"NLOS Identification and Mitigation for Time-based Indoor Localization Systems: Survey and Future Research Directions","volume":"56","author":"Nkrow","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110040","DOI":"10.1016\/j.sigpro.2025.110040","article-title":"A robust TDOA localization method for researching upper bound on NLOS ranging error","volume":"235","author":"Shui","year":"2025","journal-title":"Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111199","DOI":"10.1016\/j.comnet.2025.111199","article-title":"A survey and future outlook on indoor location fingerprinting privacy preservation","volume":"262","author":"Fathalizadeh","year":"2025","journal-title":"Comput. Netw."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1145\/3657639","article-title":"Exploiting Anchor Links for NLOS Combating in UWB Localization","volume":"20","author":"Chen","year":"2024","journal-title":"ACM Trans. Sen. Netw."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, Y., and Wang, M. (2022). The LOS\/NLOS classification method based on deep learning for the UWB localization system in coal mines. Appl. Sci., 12.","DOI":"10.3390\/app12136484"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s11235-019-00572-w","article-title":"LOS\/NLOS channel identification for improved localization in wireless ultra-wideband networks","volume":"72","author":"Landolsi","year":"2019","journal-title":"Telecommun. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"28995","DOI":"10.1109\/JSEN.2024.3434329","article-title":"WDMA-UWB Indoor Positioning Through Channel Classification-Based NLOS Mitigation Approach","volume":"24","author":"Liu","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102714","DOI":"10.1016\/j.phycom.2025.102714","article-title":"LOS\/NLOS classification using causal backtracking and ResNet in UWB sensing","volume":"72","author":"Qin","year":"2025","journal-title":"Phys. Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6487","DOI":"10.1109\/JLT.2021.3098005","article-title":"Accurate indoor visible light positioning using a modified pathloss model with sparse fingerprints","volume":"39","author":"AlMuallim","year":"2021","journal-title":"J. Light Technol."},{"key":"ref_24","unstructured":"Dahiru Buhari, M., Bagus Susilo, T., Khan, I., and Olaniyi Sadiq, B. (2023). Statistical LOS\/NLOS Classification for UWB Channels. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guo, J., Zhang, L., Wang, W., and Zhang, K. (2020, January 28\u201330). Hyperbolic Localization Algorithm in Mixed LOS-NLOS Environments. Proceedings of the 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China.","DOI":"10.1109\/ICPICS50287.2020.9202369"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Minango, J., Paredes-Parada, W., and Zambrano, M. (2021, January 1\u20133). Supervised Machine Learning Algorithms for LOS\/NLOS Classification in Ultra-Wide-Band Wireless Channel. Proceedings of the International Conference on Innovation and Research, Sangolqu\u00ed, Ecuador.","DOI":"10.1007\/978-3-031-11438-0_44"},{"key":"ref_27","first-page":"101979","article-title":"Improved UWB-based indoor positioning system via NLOS classification and error mitigation","volume":"63","author":"Wang","year":"2025","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, F., Tang, H., and Chen, J. (2023). Survey on NLOS identification and error mitigation for UWB indoor positioning. Electronics, 12.","DOI":"10.3390\/electronics12071678"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"17429","DOI":"10.1109\/ACCESS.2018.2817800","article-title":"Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices","volume":"6","author":"Bregar","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"20095","DOI":"10.1109\/ACCESS.2023.3250180","article-title":"Accurate indoor positioning for UWB-based personal devices using deep learning","volume":"11","author":"Sung","year":"2023","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Q., Chen, M., Liu, J., Lin, Y., Li, K., Yan, X., and Zhang, C. (2024). 1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System. Appl. Sci., 14.","DOI":"10.3390\/app14177609"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111191","DOI":"10.1016\/j.measurement.2022.111191","article-title":"NLOS identification using parallel deep learning model and time-frequency information in UWB-based positioning system","volume":"195","author":"Wei","year":"2022","journal-title":"Measurement"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1080\/10095020.2023.2178334","article-title":"FCN-Attention: A deep learning UWB NLOS\/LOS classification algorithm using fully convolution neural network with self-attention mechanism","volume":"27","author":"Pei","year":"2024","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"117721","DOI":"10.1016\/j.measurement.2025.117721","article-title":"A novel credibility evaluation and mitigation for ranging measurement in UWB localization","volume":"256","author":"Yang","year":"2025","journal-title":"Measurement"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1109\/LCOMM.2022.3187661","article-title":"A transformer-based signal denoising network for AoA estimation in NLoS environments","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE Commun. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"114835","DOI":"10.1016\/j.measurement.2024.114835","article-title":"The application of gated recurrent unit algorithm with fused attention mechanism in UWB indoor localization","volume":"234","author":"Tian","year":"2024","journal-title":"Measurement"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1038\/s41597-023-02639-5","article-title":"Indoor UWB positioning and position tracking data set","volume":"10","author":"Bregar","year":"2023","journal-title":"Sci. Data"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tian, Y., Lian, Z., Wang, P., Wang, M., Yue, Z., and Chai, H. (2024). Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-52464-y"},{"key":"ref_40","first-page":"8503817","article-title":"Fuzzy Transformer Machine Learning for UWB NLOS Identification and Ranging Mitigation","volume":"74","author":"Yang","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1033\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:48:41Z","timestamp":1764240521000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1033"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,27]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["info16121033"],"URL":"https:\/\/doi.org\/10.3390\/info16121033","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,27]]}}}