Robust Integrated Parallel Covariance Intersection Fusion Estimator for Uncertain AR Signal Systems with Unknown Cross-Covariances

Robust Integrated Parallel Covariance Intersection Fusion Estimator for Uncertain AR Signal Systems with Unknown Cross-Covariances | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 14 February 2025 V1 Latest version Share on Robust Integrated Parallel Covariance Intersection Fusion Estimator for Uncertain AR Signal Systems with Unknown Cross-Covariances Authors : Peng Zhang 0009-0002-3527-3052 [email protected] , Xue Liu , and Zhibo Yang Authors Info & Affiliations https://doi.org/10.22541/au.173952579.97098615/v1 134 views 70 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Robust weighted fusion estimator for AR systems with mixed uncertainties is presented, where the mixed uncertainties include uncertain noise variances and missing measurements and multiplicative noises . The design approach of integrated parallel covariance intersection fusion predictor has three steps, which includes model conversion, the design of local and parallel covariance intersection fusion predictor and the confirmation of their robustness. By the state space and the fictious approach, the original system is converted into a multi-model system. According to the mini-max robust estimation principle and the parallel covariance intersection fusion algorithm, the local and fusion predictors are presented. The robustness and the robust accuracies of them are proved by matric conversion method. A simulation example verifies the correctness and effectiveness of the proposed results. Supplementary Material File (template1220.docx) Download 901.44 KB Information & Authors Information Version history V1 Version 1 14 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ar signal integrated parallel covariance intersection minimax robust estimation uncertain noise variance Authors Affiliations Peng Zhang 0009-0002-3527-3052 [email protected] Beihua University View all articles by this author Xue Liu The PLA 91550 Troop Liaoning China View all articles by this author Zhibo Yang The PLA 91550 Troop Liaoning China View all articles by this author Metrics & Citations Metrics Article Usage 134 views 70 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Peng Zhang, Xue Liu, Zhibo Yang. Robust Integrated Parallel Covariance Intersection Fusion Estimator for Uncertain AR Signal Systems with Unknown Cross-Covariances. Authorea . 14 February 2025. DOI: https://doi.org/10.22541/au.173952579.97098615/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. 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