[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82369-en":3,"doc-seo-82369-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82369,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Event-triggered Parameter Estimator for Sensor Fusion","This paper studies event-triggered parameter estimation in sensor fusion systems where sensors transmit measurements to a gradient-based estimator. A regressor-driven local triggering rule eliminates the need for knowing the current parameter estimate and uses only regressor signals. Under persistent excitation of the aggregate regressor, explicit design inequalities define estimator gain and event thresholds to guarantee global exponential convergence. A time-varying Lyapunov analysis is used, together with a condition on regressor dynamics to ensure uniformly bounded inter-event times and avoid Zeno behavior. Simulations demonstrate communication savings while preserving exponential convergence.","10 Jul 2026  \nEvent-triggered parameter estimator for sensor fusion  \nAriana M´endez-Castillo ∗ ,∗∗ Irene Perez-Salesa ∗∗ Rodrigo Aldana-L´opez ∗∗ Antonio Ram´ırez-Trevi˜no ∗  \nRosario Aragues ∗∗  \n∗ Department of Electrical Engineering, Cinvestav-Guadalajara, Jalisco, 45019 M´exico (e-mail: [ariana.mendez@cinvestav.mx](ariana.mendez@cinvestav.mx),  \n[antonio.ramirezt@cinvestav.mx](antonio.ramirezt@cinvestav.mx))∗∗ Department of Computing and Systems Engineering - I3A,  \nUniversity of Zaragoza, Zaragoza 50009 Espa˜na (e-mail: [i.perez@unizar.es](i.perez@unizar.es), [raldana@unizar.es](raldana@unizar.es), raragues@unizar.es )  \nAbstract: This paper studies event-triggered parameter estimation in sensor fusion systems where sensors transmit measurements to a gradient based estimator. We introduce a regressordriven local triggering rule that requires no knowledge of the current parameter estimate and depends solely on the regressor signals. Under a persistent excitation condition on the aggregate regressor, we derive explicit design inequalities on the estimator gain and event thresholds that guarantee global exponential convergence. The analysis is based on a timevarying Lyapunov function. We further provide a sufficient condition on the regressor dynamics that enforces a uniform lower bound on inter-event times, excluding Zeno behavior. Simulations show substantial communication savings while preserving exponential convergence.  \nKeywords: Parameter estimator, event-triggered mechanism, sensor fusion, gradient estimator.  \narXiv :2607 .09496v1  \nproblem of estimating unknown parameters from measurements collected by a network of sensors, while aiming to reduce the overall use of the communication network.  \nSeveral estimation algorithms exist in the literature. A standard approach relies on modeling the unknown parameters as constant states of an underlying dynamical system. Therefore, one may apply a discrete-time Kalman filter (Kalman, 1960; Maybeck, 1982) or a continuoustime Kalman–Bucy filter (Golovan and Matasov, 1994) . In both cases, the covariance of the estimation error evolves according to a Riccati equation, which guarantees  \n⋆ This work was supported in part by the Secretar´ıa de Ciencia, Humanidades, Tecnolog´ıa e Innovaci´on (SECIHTI), M´exico, previously administered by the Consejo Nacional de Humanidades, Ciencias y Tecnolog´ıas (CONAHCYT) with grant number 1229622, and in part by projects PID2021-124137OB-I00 and PID2024- 159279OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU, by project REMAIN S1/1.1/E0111 (Interreg Sudoe Programme, ERDF), and via project DGA T45 23R (Gobierno de Arag´on) . Grant reference BG24/00121 funded by MICIU/AEI/10.13039/501100011033 . ©2026 IFAC. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND. Accepted for presentation at the 23rd IFAC World Congress 2026 .  \nasymptotic convergence of the estimation error. To obtain exponential convergence, different estimators have been introduced, most notably gradient-based schemes (Ioannou and Sun, 1996; Sastry and Bodson, 2011) . For this class of estimators, exponential convergence of the parameter estimates to their true values is guaranteed under the standard persistent excitation (PE) condition (Anderson, 1977) . However, classical parameter estimation methods are primarily formulated for settings in which sensors and estimators can interact instantaneously.  \nIn contrast, sensor networks, where sensing units and processing units are often spatially distributed, arise in many modern applications. In such networks, the information available at any individual sensor is often insufficient to estimate the unknown parameters of interest. As a result, the measurements collected across the network are transmitted to an estimator and processed collectively, a strategy commonly referred to as sensor fusion (Sasiadek, 2002) . This approach is a core component in the design of autonomous ","cbCaiiy49mocqF0l","https://ap.wps.com/l/cbCaiiy49mocqF0l","pdf",495512,3,1,6,"English","en",105,"# Abstract\n# Motivation and Background\n## Parameter Estimation and Persistent Excitation\n## Sensor Networks and Sensor Fusion\n# Related Work on Event-Triggered Mechanisms","[{\"question\":\"What problem does the paper address in sensor fusion systems?\",\"answer\":\"The paper addresses estimating unknown parameters from measurements collected by a network of sensors while reducing communication usage by triggering transmissions only when necessary.\"},{\"question\":\"How does the proposed event-triggering rule work?\",\"answer\":\"It uses a regressor-driven local triggering rule that depends solely on regressor signals and does not require knowledge of the current parameter estimate.\"},{\"question\":\"What conditions ensure exponential convergence and prevent Zeno behavior?\",\"answer\":\"Persistent excitation of the aggregate regressor enables explicit design inequalities for gain and thresholds guaranteeing global exponential convergence. A sufficient condition on regressor dynamics provides a uniform lower bound on inter-event times, excluding Zeno behavior.\"}]",1784179967,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"event-triggered-parameter-estimator-for-sensor-fusion","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/event-triggered-parameter-estimator-for-sensor-fusion/82369/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in sensor fusion systems?","Question",{"text":75,"@type":76},"The paper addresses estimating unknown parameters from measurements collected by a network of sensors while reducing communication usage by triggering transmissions only when necessary.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed event-triggering rule work?",{"text":80,"@type":76},"It uses a regressor-driven local triggering rule that depends solely on regressor signals and does not require knowledge of the current parameter estimate.",{"name":82,"@type":73,"acceptedAnswer":83},"What conditions ensure exponential convergence and prevent Zeno behavior?",{"text":84,"@type":76},"Persistent excitation of the aggregate regressor enables explicit design inequalities for gain and thresholds guaranteeing global exponential convergence. 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