{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:42:38Z","timestamp":1766486558984,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M731676"],"award-info":[{"award-number":["2023M731676"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Funding Program for Excellent Postdoctoral Talent","award":["2023M731676"],"award-info":[{"award-number":["2023M731676"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the problem of distributed state estimation in sensor networks, a novel optimal distributed finite-time fusion filtering method based on dynamic communication weights has been developed. To tackle the fusion errors caused by incomplete node information in distributed sensor networks, the concept of limited iterations of global information aggregation was introduced, namely, fast finite-time convergence techniques. Firstly, a local filtering algorithm architecture was constructed to achieve fusion error convergence within a limited number of iterations. The maximum number of iterations was derived to be the diameter of the communication topology graph in the sensor network. Based on this, the matrix weight fusion was used to combine the local filtering results, thereby achieving optimal estimation in terms of minimum variance. Next, by introducing the generalized information quality (GIQ) calculation method and associating it with the local fusion result bias, the relative communication weights were obtained and embedded in the fusion algorithm. Finally, the effectiveness and feasibility of the proposed algorithm were validated through numerical simulations and experimental tests.<\/jats:p>","DOI":"10.3390\/s23177397","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:47:08Z","timestamp":1692874028000},"page":"7397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3873-6868","authenticated-orcid":false,"given":"Hang","family":"Yu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5729-798X","authenticated-orcid":false,"given":"Keren","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3385-8815","authenticated-orcid":false,"given":"Qingyu","family":"Li","sequence":"additional","affiliation":[{"name":"North Information Control Research Academy Group Co., Ltd., Nanjing 211153, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bates, H., Pierce, M., and Benter, A. (2021). Real-time environmental monitoring for aquaculture using a LoRaWAN-based IoT sensor network. Sensors, 21.","DOI":"10.3390\/s21237963"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15071","DOI":"10.1109\/JSEN.2021.3136546","article-title":"Sensor selection for maneuvering target tracking in wireless sensor networks with uncertainty","volume":"22","author":"Li","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hasanujjaman, M., Chowdhury, M.Z., and Jang, Y.M. (2023). Sensor fusion in autonomous vehicle with traffic surveillance camera system: Detection, localization, and AI networking. Sensors, 23.","DOI":"10.3390\/s23063335"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1002\/int.22318","article-title":"A novel dynamic weight allocation method for multisource information fusion","volume":"36","author":"Li","year":"2021","journal-title":"Int. J. Intell. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/03081079.2018.1543667","article-title":"Dynamic weights allocation according to uncertain evaluation information","volume":"48","author":"Jin","year":"2019","journal-title":"Int. J. Gen. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106747","DOI":"10.1016\/j.ress.2019.106747","article-title":"Bayesian entropy network for fusion of different types of information","volume":"195","author":"Wang","year":"2020","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, R., Yi, X., Yu, L., Zhang, C., Wang, T., and Zhang, X. (2022). Infrasound Source Localization of Distributed Stations Using Sparse Bayesian Learning and Bayesian Information Fusion. Remote Sens., 14.","DOI":"10.3390\/rs14133181"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Neaz, A., Lee, E.H., Jin, T.H., Cho, K.C., and Nam, K. (2023). Optimizing Yarn Tension in Textile Production with Tension\u2013Position Cascade Control Method Using Kalman Filter. Sensors, 23.","DOI":"10.3390\/s23125494"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hoang, M.L., Carrat\u00f9, M., Paciello, V., and Pietrosanto, A. (2023). Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking. Sensors, 23.","DOI":"10.3390\/s23125603"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.ast.2003.08.003","article-title":"Multi-sensor optimal information fusion Kalman filters with applications","volume":"8","author":"Sun","year":"2004","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Carli, R., Chiuso, A., Schenato, L., and Zampieri, S. (2007, January 12\u201314). Distributed Kalman filtering using consensus strategies. Proceedings of the 2007 46th IEEE Conference on Decision and Control, New Orleans, LA, USA.","DOI":"10.1109\/CDC.2007.4434667"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1109\/TAC.2013.2246476","article-title":"Distributed estimation for moving target based on state-consensus strategy","volume":"58","author":"Zhou","year":"2013","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1049\/iet-spr.2012.0274","article-title":"Distributed consensus-based Kalman filtering in sensor networks with quantised communications and random sensor failures","volume":"8","author":"Song","year":"2014","journal-title":"IET Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4458","DOI":"10.1109\/TSP.2015.2424205","article-title":"Distributed Kalman filtering with dynamic observations consensus","volume":"63","author":"Das","year":"2015","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10795","DOI":"10.1109\/ACCESS.2018.2809451","article-title":"Distributed Kalman filtering with finite-time max-consensus protocol","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1080\/00207721.2013.873836","article-title":"Distributed Kalman filtering via node selection in heterogeneous sensor networks","volume":"46","author":"Petitti","year":"2015","journal-title":"Int. J. Syst. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Thia, J., Yuan, Y., Shi, L., and Gon\u00e7alves, J. (2013, January 10\u201313). Distributed Kalman Filter with minimum-time covariance computation. Proceedings of the 52nd IEEE Conference on Decision and Control, Firenze, Italy.","DOI":"10.1109\/CDC.2013.6760174"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.automatica.2018.05.012","article-title":"A distributed Kalman filtering algorithm with fast finite-time convergence for sensor networks","volume":"95","author":"Wu","year":"2018","journal-title":"Automatica"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yin, H., Li, D., Wang, Y., and Hong, X. (2022). Adaptive Data Fusion Method of Multisensors Based on LSTM-GWFA Hybrid Model for Tracking Dynamic Targets. Sensors, 22.","DOI":"10.3390\/s22155800"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"120937","DOI":"10.1109\/ACCESS.2019.2937114","article-title":"Short-term passenger flow prediction in urban public transport: Kalman filtering combined k-nearest neighbor approach","volume":"7","author":"Liang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4890","DOI":"10.1007\/s00034-020-01393-y","article-title":"Application of improved BP neural network in information fusion Kalman filter","volume":"39","author":"Yang","year":"2020","journal-title":"Circuits Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3075","DOI":"10.1109\/TIE.2016.2636814","article-title":"Fusion Kalman\/UFIR filter for state estimation with uncertain parameters and noise statistics","volume":"64","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/TCYB.2021.3110587","article-title":"Distributed set-membership fusion filtering for nonlinear 2-D systems over sensor networks: An encoding\u2013decoding scheme","volume":"53","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/9.364","article-title":"Decentralized structures for parallel Kalman filtering","volume":"33","author":"Hashemipour","year":"1988","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1016\/j.automatica.2004.01.014","article-title":"Multi-sensor optimal information fusion Kalman filter","volume":"40","author":"Sun","year":"2004","journal-title":"Automatica"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1109\/TAC.2004.834113","article-title":"Consensus problems in networks of agents with switching topology and time-delays","volume":"49","author":"Murray","year":"2004","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2021.02.011","article-title":"A trusted consensus fusion scheme for decentralized collaborated learning in massive IoT domain","volume":"72","author":"Wang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, P., Zhou, S., Zhang, P., and Li, M. (2023). Distributed State Fusion Estimation of Multi-Source Localization Nonlinear Systems. Sensors, 23.","DOI":"10.3390\/s23020698"},{"key":"ref_29","first-page":"433","article-title":"An efficient distributed Kalman filter over sensor networks with maximum correntropy criterion","volume":"8","author":"Hu","year":"2022","journal-title":"IEEE Trans. Signal Inf. Process. Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.inffus.2022.11.016","article-title":"Distributed minimum error entropy Kalman Filter","volume":"91","author":"Feng","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9061","DOI":"10.1016\/j.jfranklin.2021.09.018","article-title":"Distributed cooperative guidance law for multiple missiles with input delay and topology switching","volume":"358","author":"Yu","year":"2021","journal-title":"J. Frankl. Inst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107450","DOI":"10.1016\/j.ast.2022.107450","article-title":"Three-dimensional adaptive fixed-time cooperative guidance law with impact time and angle constraints","volume":"123","author":"Yu","year":"2022","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.inffus.2016.02.005","article-title":"An intelligent quality-based approach to fusing multi-source probabilistic information","volume":"31","author":"Yager","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1002\/int.22111","article-title":"Aggregation of uncertainty data based on ordered weighting aggregation and generalized information quality","volume":"34","author":"Li","year":"2019","journal-title":"Int. J. Intell. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.automatica.2018.03.029","article-title":"Consistent distributed state estimation with global observability over sensor network","volume":"92","author":"He","year":"2018","journal-title":"Automatica"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7397\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:38:34Z","timestamp":1760128714000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/17\/7397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,24]]},"references-count":35,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23177397"],"URL":"https:\/\/doi.org\/10.3390\/s23177397","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,8,24]]}}}