Presenter Information

Louis Tonc, Utah State University

Location

Orbital ATK Conference Center

Start Date

5-7-2018 9:30 AM

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.

Comments

Session 2

Available for download on Tuesday, May 07, 2019

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May 7th, 9:30 AM

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.