Algoritmo de calibração de magnetômetros triaxiais utilizando ajuste de quádrica por distância algébrica

Detalhes bibliográficos
Ano de defesa: 2017
Autor(a) principal: Mucciaccia, Sérgio Silva
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Espírito Santo
BR
Mestrado em Engenharia Elétrica
Centro Tecnológico
UFES
Programa de Pós-Graduação em Engenharia Elétrica
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://repositorio.ufes.br/handle/10/6913
Resumo: The measurements obtained from magnetometers are sensitive to disturbances and errors, requiring a calibration method that can considerably improve accuracy. The ellipsoid fitting is one of the most widely used methods for magnetometer calibration, but most algorithms use iterative methods, causing runtime and convergence problems. As an alternative, a direct algorithm based on the method of least squares using the algebraic distance metric is proposed. This present work presents an algorithm of calibration of magnetometers and its use in a system of calibration and fusion of data of magnetometers, accelerometers and gyroscopes based on a Kalman filter forming an inertial sensor able to obtain its orientation in the space. Computational simulations and tests with real data show that the calibration algorithm eliminates almost all the linear errors while performing much faster than traditional algorithms. Measurements of a magnetometer calibrated with the proposed algorithm are used in conjunction with measurements from accelerometers and gyroscopes to form an inertial measurement unit (IMU) using a simple Kalman filter. The complete system worked as expected and the test results indicate that the magnetometer calibration algorithm is suitable for use in an IMU being more than ten times faster than traditional algorithms and presenting similar accuracy