Kalman Filter for Satellite Attitude Determination

This article provides a comprehensive overview of Kalman filtering for satellite attitude determination, including detailed MATLAB simulation implementation. The Kalman filter represents a mathematical algorithm designed for estimating unknown signal states, with widespread applications across navigation systems, image processing, and robotic control domains. For satellite attitude determination, the Kalman filter enables precise estimation of spacecraft orientation, facilitating improved control over satellite motion and positioning. The MATLAB simulation environment allows developers to implement quaternion-based or Euler angle representations while incorporating sensor noise models through functions like 'randn' for Gaussian noise simulation. Key implementation aspects include state transition matrix formulation using satellite dynamics equations, measurement models integrating gyroscope and star tracker data, and covariance propagation through recursive prediction-correction cycles. MATLAB's implementation typically involves functions such as 'filter' or custom algorithms handling matrix operations for state estimation, with visualization tools enabling real-time monitoring of attitude convergence and estimation error analysis. This simulation approach provides valuable insights into the attitude determination process while enabling verification and calibration of algorithmic performance under various operational scenarios.