Multiple Model Recursive Gaussian Process for Robust Target Tracking

Accurate tracking of targets is vital for safe and reliable operations, particularly in complex and dynamic environments such as urban areas. Traditional tracking methods, including Kalman and particle filters, often perform poorly in real world scenarios, due to inaccurate models and sparse or noisy measurements. Gaussian process (GP) based methods offer a flexible and data driven alternative with uncertainty quantification that does not depend on predefined dynamical equations. However, state of the art GP tracking approaches require expensive hyperparameter optimization, which limits their practicality for real time applications. In this work, we introduce a novel GP mixture based computationally efficient tracking method, which is capable of modeling complex system behavior and adapt to changing dynamics. Our proposed solution, named Multiple Model Recursive Gaussian Process (MM-RGP), adapts continuously to changing dynamics, is capable of modeling complex behavior, and is robust against sparse observation. In addition, the proposed method avoids hyperparameter optimization and adapts to incoming data. We demonstrate the effectiveness of our solution using the example of unmanned aerial vehicle (UAV) tracking, with both simulated and real datasets, and propose directions for extending our work.