Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.

• Ano de defesa: 2004
• Autor(a) principal: Julio Cesar Bolzani de Campos Ferreira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Instituto Tecnológico de Aeronáutica
• Palavras-chave em Português: Filtros de Kalman; Fusão de multisensor; Veículos de lançamento; Estimação de sistemas; Radar; Engenharia aeroespacial; Engenharia eletrônica
• Link de acesso: http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=679

This work focuses on tracking launch vehicles with multiple radar sites and proposes a data fusion strategy based on the Covariance Intersection (CI) method. At each site, multiple models are embedded in a Kalman filter or locally estimate position, velocity, and acceleration using a de-biased measurement transformation from spherical to cartesian coordinates. The estimation of position, velocity, and acceleration of moving object based on radar measurements is critical in applications such as air traffic control, surveillance systems, and orbital vehicles launching among many others. However, this work will focus on the conditions observed at Alcântara Launch Center, where two radars located at distinct sites provide the trajectory coverage. All simulations presented herein make use of actual data obtained from a VS30 sounding rocket launch at Alcântara Launch Center in February, 2000. Impact point prediction is also assessed, and considers uncertainties inferred form the computed covariance matrix, culminating in an ellipsoidal impact area with a given impact probability.