Tracking a maneuvering target requires the modeling of the target's movements by multiple pre-defined mathematical models. However, the uncertainty in the target's dynamics can lead traditional model-based (MB) tracking algorithms to significant performance degradation when model mismatch occurs. To tackle this problem, we propose the use of a Recurrent Neural Network (RNN) for the purpose of learning complex target dynamics. Following the recent advances in state estimation provided by KalmanNet, a neural network-aided Kalman Filter, the proposed approach aims to exploit its tracking performance in a multiple model schema to compensate for model mismatch across maneuvers, leading to a more prompt response to motion switches. The results over a simulated set of maneuvering target trajectories demonstrate the potential of the proposed approach over the MB solution.
