Target Tracking Using a Time-Varying Autoregressive Dynamic Model

Abstract

Target tracking algorithms commonly use structured dynamic models which require prior training of fixed model parameters. These trackers have reduced accuracy if the target behaviour does not match the dynamic model. This work develops an algorithm that can infer target dynamic behaviour online, allowing the target dynamic to be time-varying as well. A time-varying target dynamic allows the target to change its level of maneuverability continuously through the trajectory, so the trajectory may have highly variable levels of agility. The developed tracker assumes the target dynamic can be described by an autoregressive model with time-varying parameters and constant, but unknown innovation variance. The autoregressive coefficients and innovation variance are then inferred online while simultaneously tracking the target. A data-association model is included to allow for clutter in the target measurements. This tracker is then compared against common structured trackers and is shown that it can approximate these models, while also showing better state filtering and prediction accuracy for an agile target.