{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T09:03:59Z","timestamp":1762851839364,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Enhancing the positioning stability and accuracy of autonomous following systems poses a significant challenge, particularly in dynamic indoor environments susceptible to occlusion and interference. This paper proposes an innovative approach that integrates Ultra-Wideband (UWB) technology with computer vision-based gait analysis to overcome these limitations. First, a low-power, high-update-rate UWB positioning network is established based on an optimized Double-Sided Two-Way Ranging (DS-TWR) protocol. To compensate for UWB\u2019s deficiencies under Non-Line-of-Sight (NLOS) conditions, a visual gait recognition process utilizing the GaitPart framework is introduced for target identification and relative motion estimation. Subsequently, an Extended Kalman Filter (EKF) is developed to seamlessly fuse absolute UWB measurements with gait-based relative kinematic information, thereby generating precise and robust estimates of the leader\u2019s trajectory. This estimated path is tracked by a differentially driven mobile platform via a Model Predictive Controller (MPC). Experimental results demonstrate that the tracking deviation for most trajectory points remains within 50 mm, with a maximum observed deviation of 115 mm during turns, confirming its strong robustness and practical utility in real-world intelligent vehicle applications.<\/jats:p>","DOI":"10.3390\/s25226869","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:07:31Z","timestamp":1762787251000},"page":"6869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Object Detection and Tracking Methods for aLow-Speed Mobile Platform"],"prefix":"10.3390","volume":"25","author":[{"given":"Gang","family":"Liu","sequence":"first","affiliation":[{"name":"Automotive Parts Advanced Manufacturing Technology Education Key Laboratory, Chongqing University of Technology, Chongqing 400054, China"},{"name":"Chongqing Zhuang Zhou Technology Co., Ltd., Chongqing 401120, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Automotive Parts Advanced Manufacturing Technology Education Key Laboratory, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Ye","sequence":"additional","affiliation":[{"name":"Automotive Parts Advanced Manufacturing Technology Education Key Laboratory, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Xu","sequence":"additional","affiliation":[{"name":"Automotive Parts Advanced Manufacturing Technology Education Key Laboratory, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Automotive Parts Advanced Manufacturing Technology Education Key Laboratory, Chongqing University of Technology, Chongqing 400054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kwon, J.W., Lee, H., Lee, J., Lee, N.H., Kim, J.C., Uhm, T., and Choi, Y.H. (2024). UWB-based human-following system with obstacle and crevasse avoidance for polar-exploration robots. Appl. Sci., 14.","DOI":"10.3390\/app14166918"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1049\/ipr2.12062","article-title":"A robust tracking algorithm for a human-following mobile robot","volume":"15","author":"Tsai","year":"2021","journal-title":"IET Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1109\/TCST.2023.3331553","article-title":"Adaptive image-based moving-target tracking control of wheeled mobile robots with visibility maintenance and obstacle avoidance","volume":"32","author":"Dai","year":"2023","journal-title":"IEEE Trans. Control. Syst. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Algabri, R., and Choi, M.-T. (2020). Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature. Sensors, 20.","DOI":"10.3390\/s20092699"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"150311","DOI":"10.1109\/ACCESS.2020.3016472","article-title":"A robust autonomous following method for mobile robots in dynamic environments","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, T., Su, Y., Shao, S., Yao, C., and Wang, Z. (October, January 27). GR-Fusion: Multi-sensor Fusion SLAM for Ground Robots with High Robustness and Low Drift. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636232"},{"key":"ref_7","unstructured":"Naghdi, S. (2020). Improving Bluetooth-Based Indoor Positioning Using Vision and Artificial Networks. [Ph.D. Thesis, University of Calgary]."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Park, C., and Rhee, S.H. (2017, January 18\u201320). Indoor positioning using Wi-Fi fingerprint with signal clustering. Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Republic of Korea.","DOI":"10.1109\/ICTC.2017.8190791"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17921","DOI":"10.1109\/ACCESS.2021.3052316","article-title":"A survey on indoor vehicle localization through RFID technology","volume":"9","author":"Motroni","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bencak, P., Hercog, D., and Lerher, T. (2022). Indoor positioning system based on bluetooth low energy technology and a nature-inspired optimization algorithm. Electronics, 11.","DOI":"10.3390\/electronics11030308"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"70219","DOI":"10.1109\/ACCESS.2022.3187410","article-title":"An overview of UWB standards and organizations (IEEE 802.15. 4, FiRa, Apple): Interoperability aspects and future research directions","volume":"10","author":"Coppens","year":"2022","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"123843","DOI":"10.1016\/j.eswa.2024.123843","article-title":"Gender classification based on gait analysis using ultrawide band radar augmented with artificial intelligence","volume":"249","author":"Saleem","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pan, J.R., Dou, Y.G., Huang, X.D., Xu, J.H., Chen, J.B., and Xie, L. (2020, January 27\u201329). Research on intelligent vehicle following technology based on UWB. Proceedings of the 2020 39th Chinese Control Conference (CCC), Shenyang, China.","DOI":"10.23919\/CCC50068.2020.9189543"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dabove, P., Di Pietra, V., Piras, M., Jabbar, A.A., and Kazim, S.A. (2018, January 23\u201326). Indoor positioning using Ultra-wide band (UWB) technologies: Positioning accuracies and sensors\u2019 performances. Proceedings of the 2018 IEEE\/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, USA.","DOI":"10.1109\/PLANS.2018.8373379"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"44413","DOI":"10.1109\/ACCESS.2022.3169267","article-title":"Precision positioning for smart logistics using ultra-wideband technology-based indoor navigation: A review","volume":"10","author":"Elsanhoury","year":"2022","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ryka\u0142a, \u0141., Typiak, A., and Typiak, R. (2020). Research on developing an outdoor location system based on the ultra-wideband technology. Sensors, 20.","DOI":"10.3390\/s20216171"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/JSAC.2010.100907","article-title":"NLOS identification and mitigation for localization based on UWB experimental data","volume":"28","author":"Marano","year":"2010","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Le Minh, T., and Xuan, D.T. (2021, January 15\u201316). Applying Kalman filter to UWB positioning with DS-TWR method in LOS\/NLOS scenarios. Proceedings of the 2021 International Symposium on Electrical and Electronics Engineering (ISEE), Ho Chi Minh, Vietnam.","DOI":"10.1109\/ISEE51682.2021.9418707"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"012022","DOI":"10.1088\/1742-6596\/2998\/1\/012022","article-title":"UWB-based 3D Localization using Least Squares Trilateration with Combination of Median Filter and Kalman Filter","volume":"2998","author":"Shukri","year":"2025","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, T., and Park, T.H. (2020). Extended Kalman filter (EKF) design for vehicle position tracking using reliability function of radar and lidar. Sensors, 20.","DOI":"10.3390\/s20154126"},{"key":"ref_21","first-page":"3467","article-title":"GaitSet: Cross-view gait recognition through utilizing gait as a deep set","volume":"44","author":"Chao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","unstructured":"Hema, M., and Esther, R.K. (2020, January 10\u201312). Gait energy image projections based on gender detection using support vector machines. Proceedings of the 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India."},{"key":"ref_23","unstructured":"Kumanomido, Y., and Hirahara, M. (2015, January 24\u201329). Gait recognition using recurrent neural networks. Proceedings of the 31st Fuzzy System Symposium, Vancouver, BC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Winter, D.A. (2009). Biomechanics and Motor Control of Human Movement, John Wiley & Sons.","DOI":"10.1002\/9780470549148"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, C., Jiang, H., Yang, X., and Wei, Q. (2025). Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles. Appl. Sci., 15.","DOI":"10.3390\/app15105464"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Song, Y., Jing, H., Liu, J., Feng, H., Lin, Z., and Li, M. (2024, January 18\u201320). MPC-Based Trajectory Tracking Control for Independent Drive Vehicle with Differential Steering. Proceedings of the 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN), Beijing, China.","DOI":"10.1109\/INDIN58382.2024.10774423"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s10846-022-01745-7","article-title":"Wheeled mobile robots: State of the art overview and kinematic comparison among three omnidirectional locomotion strategies","volume":"106","author":"Tagliavini","year":"2022","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhou, Z., and Bao, Y. (2024, January 23\u201325). Path tracking control of autonomous vehicle based on MPC. Proceedings of the 2024 3rd International Conference on Energy and Power Engineering, Control Engineering (EPECE), Chengdu, China.","DOI":"10.1109\/EPECE63428.2024.00046"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Marrugo, D.A., and Villa, J.L. (2023). MPC-Based Path Tracking of a Differential-Drive Mobile Robot with Optimization for Improved Control Performance. Workshop on Engineering Applications, Springer Nature.","DOI":"10.1007\/978-3-031-46739-4_25"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/22\/6869\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T09:01:32Z","timestamp":1762851692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/22\/6869"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":29,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["s25226869"],"URL":"https:\/\/doi.org\/10.3390\/s25226869","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,11,10]]}}}