Augmented physics-based machine learning for navigation and tracking

Abstract

This article presents a survey of the use of AI/ML techniques in navigation and tracking applications, with a focus on the dynamical models typically involved in corresponding state estimation problems. When physics-based models are either not available or not able to capture the complexity of the actual dynamics, recent works explored the use of deep learning models. This article tradeoffs both models and presents promising solutions in between, whereby physics-based models are augmented by data-driven components. The article uses two target tracking examples, both with syntethic and real data, to illustrate the various choices of the models and their parameters, highlighting their benefits and challenges. Finally, the paper provides some conclusions and an outlook for future research in this relevant area.