{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T02:41:22Z","timestamp":1777603282843,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T00:00:00Z","timestamp":1754956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of Henan Province, China","award":["202102210297"],"award-info":[{"award-number":["202102210297"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>This paper investigates the joint state and parameter estimation issue of the bilinear state\u2013space system with non-Gaussian process noise and non-Gaussian measurement noise. Tackling such an issue is challenging because either of these noises may seriously degrade the estimation performance. To significantly counteract the negative effect of these non-Gaussian noises, a Gaussian\u2013Versoria mixed kernel correntropy (GVMKC)-based cost function is introduced by integrating two different types of kernel functions into a mixed kernel. Subsequently, a GVMKC-based Kalman filtering and a GVMKC-based robust recursive least squares method are derived for estimating the system states and parameters, respectively. Thus, a robust joint parameter and state estimation method is developed by implementing the interactive computation. The effectiveness of the proposed method is confirmed by simulation examples.<\/jats:p>","DOI":"10.3390\/axioms14080630","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T16:30:36Z","timestamp":1755016236000},"page":"630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Gaussian\u2013Versoria Mixed Kernel Correntropy-Based Robust Parameter and State Estimation for Bilinear State\u2013Space Systems with Non-Gaussian Process and Measurement Noises"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6503-4066","authenticated-orcid":false,"given":"Xuehai","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijuan","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100993","DOI":"10.1016\/j.arcontrol.2025.100993","article-title":"Hierarchical generalized extended parameter identification for multivariable equation-error ARMA-like systems by using the filtering identification idea","volume":"60","author":"Ding","year":"2025","journal-title":"Annu. 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