8482
2017-09-16
Advanced Filtering Techniques Applied to Spaceflight, Phase I
Completed
Jan 2010
Jul 2010
Spacecraft need accurate position and velocity estimates in order to control their orbits. Some missions require more accurate estimates than others, but nearly all missions need some type of orbit determination. IST-Rolla seeks to provide highly accurate algorithms that do not overpower the spacecraft's computer. Many new, powerful algorithms exist such as the particle filter and the unscented Kalman filter, but most of them involve integrating several state vectors, and those integrations devour the computing power available. IST-Rolla will implement the รจ-D technique, the cost based filter (CBF), and the neural network estimator for orbit determination(developed by IST-Rolla Engineers) and analyze the results. These filters are nonlinear and might provide better accuracy than the extended Kalman filter (EKF) which is widely used, without being computationally cumbersome as the particle filter and unscented Kalman filter. The theta-D technique approximates the solution to the filter-related Ricatti Equation. The CBF is an attempt to formulation of the filter under an 'optimal' framework. The neural network estimator works to estimate the modeling errors online so that the estimates become more accurate.
SBIR/STTR
Space Technology Mission Directorate
Johnson Space Center
JSC
NASA Center
Houston
TX
IST-Rolla
Industry
Rolla
MO
Missouri
Texas
Therese Griebel
Carlos Torrez