Model-based tracking algorithms often suffer from significant performance degradation when tracking maneuvering targets, primarily due to inherent uncertainties in target dynamics. To address this limitation, we propose a novel ensemble-based approach that integrates multiple neural-aided Kalman filters, referred to as KalmanNet, within a multiple-model framework, inspired by traditional interacting multiple-model (IMM) filtering techniques. Each KalmanNet instance is specialized in tracking targets governed by a distinct motion model. The ensemble fuses their state estimates using a Recurrent Neural Network (RNN), which learns to adaptively weigh and combine the predictions based on the underlying target dynamics. This fusion mechanism enables the system to model complex motion patterns more effectively and achieves lower estimation bias and variance compared to relying on a single KalmanNet when tracking maneuvering targets, as demonstrated through extensive simulation experiments. Furthermore, we introduce an explainable, innovation-based attention mechanism to enhance the interpretability of our results, inspired by traditional model-based tracking algorithms, that aids the identification of target motion dynamics. Our findings indicate that this attention mechanism improves robustness to sensor noise, out-of-distribution data, and missing measurements. Overall, this innovative approach has the potential to advance state-of-the-art target tracking applications.
