Track Fusion Algorithm Based on Maximum Likelihood Estimation

Track Fusion Algorithm Based on Maximum Likelihood Estimation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Electronics Letters This is a preprint and has not been peer reviewed. Data may be preliminary. 12 May 2026 V1 Latest version Share on Track Fusion Algorithm Based on Maximum Likelihood Estimation Authors : Lijun Bian [email protected] , Nan Xue [email protected] , Dandan Kong [email protected] , Qicheng Wu [email protected] , Hongrui Zhang [email protected] , Zelin Liang [email protected] , Maolin Lu [email protected] , Qiang Guo [email protected] , Jiangying Du [email protected] , and Ning Mao 0009-0008-6176-860X [email protected] Authors Info & Affiliations https://doi.org/10.22541/authorea.15003136/v1 15 views 5 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract To address system biases and non-stationary noise in distributed radar networks, this paper proposes a robust track fusion algorithm based on a hybrid weighting mechanism. Drawing on the Interactive Multiple Model (IMM) framework, individual radar nodes are treated as parallel sub-filters. The algorithm integrates statistical maximum likelihood estimation with spatial geometric support to dynamically update fusion weights. A weight transition matrix is employed to describe the evolution of node credibility, while a Gaussian kernel-based similarity model evaluates geometric consistency to effectively suppress measurement outliers. By incorporating a mixing factor, the system adaptively balances statistical reliability and spatial support. Results indicate that this approach significantly enhances tracking accuracy and environmental adaptability, providing superior resilience against complex electromagnetic interference compared to traditional likelihood-based methods. Information & Authors Information Version history V1 Version 1 12 May 2026 Collection Electronics Letters Keywords 5G mobile communication commutation 5G mobile communication network analysis commutation radar 5G mobile communication 4G mobile communication commutation optical communication signal processing Internet of Things signal processing commutation commutation circuit analysis computing circuit testing 5G mobile communication radar sensor fusion sensorless machine control data handling radar tracking commutation sensor fusion Radar, Sonar and Navigation signal processing array signal processing data handling sensor fusion radar 5G mobile communication commutation commutation radar optical communication signal processing radar tracking commutation sensor fusion 5G mobile communication 4G mobile communication commutation Radar, Sonar and Navigation signal processing array signal processing 5G mobile communication radar Internet of Things signal processing commutation sensor fusion sensorless machine control data handling 5G mobile communication network analysis commutation circuit analysis computing circuit testing Authors Affiliations Lijun Bian [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Nan Xue [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Dandan Kong [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Qicheng Wu [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Hongrui Zhang [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Zelin Liang [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Maolin Lu [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Qiang Guo [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Jiangying Du [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Ning Mao 0009-0008-6176-860X [email protected] China Tower (Tianjin) Technology Innovation Center, Tianjin, China, 300134 View all articles by this author Metrics & Citations Metrics Article Usage 15 views 5 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lijun Bian, Nan Xue, Dandan Kong, et al. Track Fusion Algorithm Based on Maximum Likelihood Estimation. Authorea . 12 May 2026. DOI: https://doi.org/10.22541/authorea.15003136/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! 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