Lambda:Omicron - A new prediction model to track maneuvering objects
Matteo Tesori, Giorgio Battistelli, Luigi Chisci, Alfonso Maria Farina, Graziano Alfredo Manduzio · 2022 25th International Conference on Information Fusion (FUSION) · 2022
This paper deals with motion modeling for 2-dimensional tracking of a maneuvering object. Specifically, a new class of nonlinear dynamic motion models, called Lambda:Omicron, is introduced with the purpose of accurately modeling maneuvers (regarded as variations of speed and turning rate) of the moving object. These models rely on the unicycle navigation model, suitably augmented with two chains of integrators to account for the unknown speed and turning rate command inputs. Quasi-exact time-discretization of the continuous-time Lambda:Omicron models is also carried out to allow their exploitation in nonlinear recursive filters. Simulation experiments are presented to show the effectiveness of the proposed models as compared to state-of-the-art linear and nonlinear motion models for tracking of strongly maneuvering objects.