Jane Jaejeong Shin

Transformer-based Motion Model for Robust Target Tracking under Intermittent and Noisy Measurements

Target tracking under intermittent measurements is a fundamental challenge in autonomous systems. Traditional methods, including Kalman filters and deep learning-based models, often struggle when faced with sparse observations and high measurement noise. In this work, we present multiple transformer