. "Estimation, filtering and detection"@en . . "no data" . "Anotation:\n\nThis course will cover description of the uncertainty of hidden variables (parameters and state of a dynamic system) using the probability language and methods for their estimation. Based on bayesian problem formulation principles of rational behavior under uncertainty will be analyzed and used to develop algorithms for parameter estimations (ARX models, Gaussian process regression), filtering (Kalman filter) and detection (likelihood ratio theory) . We will demonstrate numerically robust implementation of the algorithms applicable in real life problems for the areas of industrial process control, robotics and avionics.\nStudy targets:\n\nAbility to solve engineering problems in the area of estimation and filtering, using rigorous theoretical background.\nContent:\n\nMS, LMS and ML estimation. Bayesian approach to uncertainty description, model of dynamic system. Identification of ARX model parameters. Tracking of time varying parameters, forgetting, prior information. Numerically robust algorithms for parameter estimation. Gaussian process regression. Stochastic system, probabilistic state definition, Kalman filter. Kalman filter for colored noise, extended Kalman filter. Stochastic dynamic programming, LQ and LQG controller, certainty equivalence principle. Fault detection and isolation methods. Likelihood ratio - theory and applications. Nonlinear estimation - local vs. global approximation. Monte Carlo methods.\nCourse outlines:\n\n1. Review of basic concepts of statistics\n2. MS, LMS and ML estimation\n3. Bayesian approach to uncertainty description, model of dynamic system\n4. Identification of ARX model parameters\n5. Tracking of time varying parameters, forgetting, prior information\n6. Numerically robust algorithms for parameter estimation\n7. Gaussian process regression\n8. Stochastic system, probabilistic state definition, Kalman filter\n9. Kalman filter for colored noise, extended Kalman filter\n10. Stochastic dynamic programming, LQ and LQG controller, certainty equivalence principle\n11. Fault detection and isolation methods\n12. Likelihood ratio - theory and applications\n13. Nonlinear estimation - local vs. global approximation\n14. Monte Carlo methods\nExercises outline:\n\nIndividual assigments - implementation of selected algorithms in Matlab, solution of individual technical problems. Deliverables: running algorithm, technical report. Homeworks: theoretical assignments. Deliverables: report." . . "no data"@en . "TRUE" . . "Others"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Master of Aerospace Engineering"@en . . "https://aerospace.fel.cvut.cz/overview" . "120"^^ . "Presential"@en . "This is a Master degree study programme focused on education and training of nowadays and/or future specialists in the field of aeronautical and space systems and technologies. Although the programme is taught at the Faculty of Electrical Engineering, it can be considered as a whole-university program, because of a strong link with the Faculty of Mechanical Engineering where several compulsory courses are given. Even if the program puts the emphasis on aerospace fields, the education is supported by a broad knowledge of electronics, embedded systems and their design, programming and usage. Moreover, the program curriculum is extended by soft skills’ training. The program content is in accordance with prestigious European aerospace universities and thus provides good competitive basis for graduates’ future employment in variety of private and state companies and institutions.\nThe study is hands-on focused. Students can thus develop their practical knowledge via practical oriented courses and individual projects. A full 4th semester of the study is dedicated to a diploma thesis which can also be solved in cooperation with industry and abroad. The CTU and program itself have strong links with European aerospace universities via PEGASUS Network which supports student exchange program and getting experience from other country.\nThe program introduces current state-of-the-art in the field of aerospace but expects graduates to be fluent also in the future technologies and systems."@en . . . "no data"@en . "FALSE" . . . "Master"@en . "Thesis" . "Not informative" . "no data"@en . "Not informative" . "Recommended" . "aircraft and spacecraft engineering, avionics, integrated systems with their subparts in terms of sensors, data processing, buses, communication, and integration, radio systems, flight control, inertial-GNSS-decision based navigation, trajectory planning."@en . "1"^^ . "FALSE" . "Upstream"@en . . . . . . . . . . . . . . . . .