Intent-informed state estimation for tracking guided targets - Lee, Seokwon; Shin, Hyo-Sang; Tsourdos, Antonios (Elsevier, 2023)
This paper proposes a state estimation and prediction for tracking guided targets using intent information. A conditionally Markov process is used to describe the destination-oriented target motion, and the collision intent is incorporated through the zero-effort-miss guidance information. The expected arrival time necessary for the conditionally Markov model is determined through the collision geometry and destination motion. Finally, the Kalman filter technique is used to estimate and predict the target state. Numerical simulations demonstrate that the proposed approach can improve state estimation accuracy in both static and dynamic destination cases.
Keywords: State estimation; Trajectory prediction; Conditionally Markov process; Kalman filtering; Predictive guidance; Intent inference