The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and model-based algorithms. The time-varying constant velocity model is integrated into the Gaussian process (GP) of online learning to improve the performance of GP prediction. This integration is further combined with a generalized probabilistic data association algorithm to realize multi-target tracking. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.

现有的机动目标跟踪方法对于杂乱环境中高机动目标的跟踪性能并不令人满意。本文提出了一种混合驱动的方法，利用数据驱动和基于模型的算法的优点来跟踪多个高机动目标。将时变等速模型融入在线学习的高斯过程（GP）中，提高GP预测的性能。这种集成进一步与广义概率数据关联算法相结合，以实现多目标跟踪。通过仿真，结果表明，与广泛使用的算法（例如交互式多模型方法和数据驱动的 GP 运动跟踪器）相比，混合驱动方法具有显着的性能改进。