Augmented Physics-Based Machine Learning for Navigation and Tracking
Tales Imbiriba, Ondřej Straka, Jindřich Duník, Pau Closas · IEEE Transactions on Aerospace and Electronic Systems · 2023
This article presents a survey of the use of artificial intelligence/machine learning (AI/ML) techniques in navigation and tracking applications, with a focus on the dynamical models typically involved in corresponding state estimation problems. Whenphysics-basedmodels are either not available or not able to capture the complexity of the actual dynamics, recent works explored the use ofdeep learningmodels. 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 synthetic and real data, to illustrate the various choices of the models and their parameters, highlighting their benefits and challenges. Finally, the article provides some conclusions and an outlook for future research in this relevant area.