Symbolic Dynamics for Radar Target Maneuver Detection With High Data Rates

In radar target tracking, knowledge of the true dynamics of target motion is paramount for accurate state estimates. Target maneuvers complicate this knowledge due to quick unknown changes in the target's dynamics. Many popular methods for detecting target maneuvers utilize an input estimation approach where the input to the target's state system is estimated. While input estimation methods work well, they are limited to lower data rate systems due to their complexity. In this work, we propose a new method of target maneuver detection using symbolic dynamics. Symbolic dynamics has the advantage of being computationally simple due to the way it symbolizes and compresses the data. We develop a new radar target maneuver detector leveraging symbolic dynamics. Through two different simulations, we demonstrate the ability of the symbolic dynamics detector to be as fast as a simple chi-squared detector while simultaneously detecting maneuvers sooner and with higher accuracy.