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Spacecraft attitude and system identification via marginal modified unscented Kalman filter utilizing the sun and calibrated three-axis-magnetometer sensors

Kiani, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. Abstract:
  3. This paper deals with the problems of attitude determination, parameter identification and reference sensor calibration simultaneously. An LEO satellite's attitude, inertia tensor as well as calibration parameters of Three-Axis-Magnetometer (TAM) including scale factors, misalignments and biases along three body axes are estimated during a maneuver designed to satisfy the condition of persistency of excitation. The advanced nonlinear estimation algorithm of Unscented Kalman Filter (UKF) is a good choice for nonlinear estimation problem of attitude determination, but its computational cost is considerably larger than the widespread low accurate Extended Kalman Filter. Reduced Sigma Point Filters provide good solutions and also decrease the run time of the UKF. However, in contrast to the nonlinear problem of attitude determination, parameter identification and sensor calibration have linear dynamics. Therefore, a new marginal UKF is proposed that combines utility of Kalman Filter with Modified UKF (MUKF) which is based on Schmidt orthogonal algorithm. The proposed Marginal MUKF (MMUKF) utilizes only 14 sigma points to achieve the complete 25-dimensional state vector estimation. Additionally, a Monte Carlo simulation has demonstrated a good accuracy and lower computational burden for concurrent estimation of attitude, inertia tensor as well as TAM calibration parameters utilizing MMUKF with respect to the sole utilization of the UKF
  4. Keywords:
  5. Attitude determination ; Inertia matrix identification ; Reduced sigma point Kalman filter ; Sensor calibration ; Unscented Kalman filter ; Calibration ; Identification (control systems) ; Intelligent systems ; Kalman filters ; Magnetometers ; Monte Carlo methods ; Nonlinear analysis ; Nonlinear filtering ; Orbits ; Tensors ; Inertia matrix ; Marginal filter ; Sigma point kalman filter ; Parameter estimation
  6. Source: Scientia Iranica ; Vol. 21, issue. 4 , 2014 , p. 1451-1460
  7. URL: http://www.scientiairanica.com/en/ManuscriptDetail?mid=265