Yoon-Yeong Kim, Black-Box Expectation-Maximization Algorithm for Estimating Latent States of High-Speed Vehicles, Master's Thesis, Department of Industrial and Systems Engineering, KAIST, 2018 

Abstract

Tracking an object under a noisy environment is difficult especially when there exist unknown parameters that affect the object’s behavior. In the case of a high-speed ballistic vehicle, the trajectory of the ballistic vehicle is affected by the change of atmospheric conditions as well as the various parameters of the object itself. To filter these latent factors of the dynamics model, this paper proposes a blackbox Expectation-Maximization algorithm to estimate the latent parameters for enhancing the accuracy of the trajectory tracking. The Expectation step calculates the likelihood of the observation by the Kalman Smoothing that reflects the forward-backward probability combination. The Maximization step optimizes the unknown parameters to maximize the likelihood by the Bayesian optimization with Gaussian processes. Our simulation results show that the error of tracking position of the ballistic vehicle reduced as proceeding these steps iteratively, and this method will reveal the key characteristics of the vehicle when we have multiple observation dataset on the high-speed vehicle with the same yet unknown specifications.

@masterthesis{Kim:2018,

author = {Yoon-Yeong Kim},

advisor ={Il-Chul Moon},

title = {Black-Box Expectation-Maximization Algorithm for Estimating Latent States of High-Speed Vehicles},

school = {KAIST},

year = {2018}

}