A Multi-Model Polynomial-Based Tracking Method for Targets with Complex Maneuvering Patterns

In the absence of a priori knowledge about target motion characteristics, the task of tracking complex maneuvering targets remains challenging. A multi-model polynomial-based complex target tracking method is presented to address this issue. Observation sequences of varying lengths are fitted by time polynomials of different orders, which are used to create a set of target motion models. Subsequently, the multi-model framework is employed to track maneuvering targets with uncertain motion characteristics. To verify the effectiveness of the suggested approach, three datasets were created with kinematic equation, the gazebo platform and real watercrafts. Based on the above three datasets, the proposed method is compared with classical multi-model methods and a deep learning method. Theoretical analysis and experimental results reveal that, in the lack of a priori information of target maneuvering features, the tracking error of the proposed method can be reduced by 12.5~30% compared with the traditional MM method. Moreover, the proposed method is able to overcome the problem of accuracy degradation caused by model misalignment and parameter tuning faced by the deep learning based methods.