CV Lattes
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Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Holds a Bachelor's degree in Control and Automation Engineering from the Federal University of Technology Paraná (UTFPR) (2017), a Master's degree (2019), and a Ph.D. (2023) in Automation from the School of Electrical and Computer Engineering at the University of Campinas (FEEC/Unicamp). Worked as an electronic engineer at the Angra dos Reis Nuclear Power Plant from November 2022 to May 2024. Since June 2024, has held the position of Assistant Professor (RDIDP, level MS-3.1) in the Department of Electrical and Computer Engineering (SEL) at the São Carlos School of Engineering (EESC) at the University of São Paulo (USP), focusing on Control and Applications. Is also an accredited faculty member in the Graduate Program in Electrical Engineering (CAPES 7) at SEL/EESC. His main research areas involve stochastic filtering, focusing on estimation theory, Kalman filtering, and nonlinear filtering. Additionally, he has experience in developing GNSS/INS inertial navigation systems using Lie group theory and tracking and association systems for radar applications. Currently, he is a member of the IEEE Control Systems Society (CSS) and the IEEE Aerospace and Electronic Systems Society (AESS). (Source: Lattes Curriculum)
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This scientific initiation proposal aims to develop a comparative study between the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) algorithms using Lie Group theory applied to the GNSS/INS navigation problem. The Kalman filter is commonly used in navigation to combine information from inertial sensors with GNSS measurements. However, it is well known that kinematic mod…
This scientific initiation proposal aims to develop a study on the problem of associating data generated by radars for multi-object tracking using neural networks. To achieve this, a two-dimensional multi-object trajectory simulator will be developed to generate synthetic signals for the training and validation of neural networks. Specifically, the simulator to be developed should be capa…
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