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Comparison between EKF and UKF for GNSS/INS navigation systems using Lie Groups

Grant number: 25/01126-5
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Start date: May 01, 2025
End date: April 30, 2026
Field of knowledge:Engineering - Electrical Engineering
Principal Investigator:Marcos Rogério Fernandes
Grantee:Antônio Duarte Pesqueira
Host Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

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 models are nonlinear, which can pose challenges for processing. Therefore, it may be necessary to adopt more sophisticated algorithms to achieve better navigation results.Recently, variations of the Kalman Filter based on Lie Groups have been developed in the literature and have proven to be more suitable for the navigation problem, as they allow for the exploration of the geometric characteristics of the navigation system. In this study, an analysis will be conducted on the behavior of EKF and UKF, implemented both conventionally and using Lie Group theory, for different trajectory profiles.This work is expected to provide a systematic understanding of the advantages and disadvantages of each filtering algorithm in the context of navigation, thereby contributing to the improvement of inertial navigation technologies. Navigation is a crucial topic for the development of new technologies, such as autonomous vehicles and the aviation industry. Furthermore, this research will allow the candidate to work with applications of signal processing techniques, dynamic system simulation, probability and statistics concepts, as well as develop and enhance their practical programming skills.

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