Monte Carlo Methods and Skewed Kalman Filters for Orbit Estimation
Session 2
Description
Under reasonable orbital conditions, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) both diverge after several updates from optical measurements. We identify the source of divergence by studying a simplified model, and correct this problem by implementing a Monte Carlo based particle filter. However, the computational cost of the particle filter is high, and future work will implement a Skewed Unscented Kalman Filter as a substitute for the particle filter. We present evidence that this skewed unscented Kalman filter will avoid divergence at a reduced cost compared to the particle filter, and discuss Monte Carlo methods for validating this claim.
Monte Carlo Methods and Skewed Kalman Filters for Orbit Estimation
Orbital ATK Conference Center
Under reasonable orbital conditions, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) both diverge after several updates from optical measurements. We identify the source of divergence by studying a simplified model, and correct this problem by implementing a Monte Carlo based particle filter. However, the computational cost of the particle filter is high, and future work will implement a Skewed Unscented Kalman Filter as a substitute for the particle filter. We present evidence that this skewed unscented Kalman filter will avoid divergence at a reduced cost compared to the particle filter, and discuss Monte Carlo methods for validating this claim.