@Article{ChagasMarCarOliHot:2021:SeAlSa,
               author = "Chagas, Ronan Arraes Jardim and Marques, Wilson Jos{\'e} de 
                         S{\'a} and Carvalho, Thadeu Augusto Medina de and Oliveira, 
                         Priscylla Ang{\'e}lica da Silva and Hott, Guilherme Mendes 
                         Cicarini",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "A self-calibration algorithm for satellite sensors based on vector 
                         observations",
              journal = "Aerospace Science and Technology",
                 year = "2021",
               volume = "114",
                pages = "e106759",
                month = "July",
             keywords = "Satellite attitude estimation, In-orbit sensor calibration, 
                         Observability analysis, Kalman filter, Vector measurements.",
             abstract = "The advance of space technology allowed small satellites to 
                         accomplish missions that were once only possible with big and 
                         expensive platforms. The quality and accuracy of small sensors 
                         have also improved, leading to a better knowledge of the 
                         spacecraft attitude. However, the integration and assembly process 
                         of such platforms has constraints that often hinder a high 
                         accuracy placement and calibration of the equipment. This 
                         translates into the three most common errors in sensor 
                         measurements: bias, misalignment, and non-orthogonality. This work 
                         proposes a new algorithm designed to estimate and correct those 
                         three error sources for any sensor based on vector observations. 
                         The algorithm is based on the same principle used by inertial 
                         navigation systems with non-inertial information. A propagator 
                         computes the attitude based on the gyro readings with the initial 
                         estimation provided by the other sensors. Concurrently, a Kalman 
                         filter estimates the attitude and sensor errors. After filter 
                         convergence, the estimation is used to correct the attitude 
                         knowledge. An observability analysis is carried out, showing in 
                         which conditions the filter can correctly estimate the error 
                         state. Afterward, the proposed technique is tested, employing a 
                         Monte Carlo simulation in a validated satellite simulator. The 
                         results show that the algorithm can significantly improve attitude 
                         estimation accuracy during different satellite operating modes. At 
                         last, the filter robustness is assessed by simulating the system 
                         with huge errors. This test shows that the filter can converge 
                         even in such a challenging scenario, providing excellent 
                         accuracy.",
                  doi = "10.1016/j.ast.2021.106759",
                  url = "http://dx.doi.org/10.1016/j.ast.2021.106759",
                 issn = "1270-9638",
             language = "en",
           targetfile = "chagas_self.pdf",
        urlaccessdate = "09 maio 2024"
}