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to develop and implement state estimation algorithms, such as Kalman filters and observers, for real-time applications. Proficiency in MATLAB and Simulink for modelling, simulation, and validation using
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Estimation & Observers: Design and implement state estimators, Extended Kalman Filters (EKF), neural observers, and physics-informed "virtual sensors" for real-time plasma state estimation and boundary
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models of steel structures with emphasis on fatigue hot-spot modelling, building on existing in-house methods Load and stress estimation using virtual sensing techniques (e.g., Kalman Filters) combined
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and ambiguity function processing Familiarity with time-, frequency-, and amplitude/phase-difference of arrival for geolocation Understanding of adaptive signal processing methods and techniques (Kalman
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and ambiguity function processing Familiarity with time-, frequency-, and amplitude/phase-difference of arrival for geolocation Understanding of adaptive signal processing methods and techniques (Kalman
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240.688.7883 Kalman Migler [email protected] 301 956 0555 Frederick R. Phelan [email protected] 301.975.6761 Paul Francis Salipante [email protected] 301 975 2820 Description We
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models of steel structures with emphasis on fatigue hot-spot modelling, building on existing in-house methods Load and stress estimation using virtual sensing techniques (e.g., Kalman Filters) combined
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techniques to estimate time differences of arrival; Development and evaluation of TDOA-based acoustic source localization algorithms; Study and implementation of target tracking techniques based on Kalman
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circuit models and algorithms for estimating the charge, health, and power based on direct methods (e.g. open circuit voltage), model-based methods (e.g. Kalman filtering), data driven methods (e.g. machine
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efforts to develop novel sensing and estimation strategies that facilitate data-driven decision support. Under the mentorship of our R&D staff, you will utilize state-of-the-art tools in machine vision, UxV