(Preprint) AAS NONLINEAR SEQUENTIAL METHODS FOR IMPACT PROBABILITY ESTIMATION
نویسندگان
چکیده
Orbit determination in application to the estimation of impact probability has the goal of determining the evolution of the state probability density function (pdf) and determining a measure of the probability of collision. Nonlinear gravitational interaction and non-conservative forces can make the pdf far from Gaussian. This work implements three nonlinear sequential estimators: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF) and the Particle Filter (PF) to estimate the impact probability. Both the EKF and the UKF make the Gaussian assumption and this work investigates the effect of this approximation on the impact probability calculation, while the PF can work for non-Gaussian systems.
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