Model-based approaches for target tracking and smoothing estimate the infinite number of possible target trajectories using a finite set of models. This paper proposes a data-driven approach that represents the possible target trajectories using a distribution over an infinite number of functions. Recursive Gaussian process and derivative based Gaussian process approaches for target tracking and smoothing are developed, with online training and parameter learning. The performance evaluation over two highly maneuvering scenarios, shows that the proposed approach provides 80% and 62% performance improvement in the position and 49% and 22% in the velocity estimation, respectively, as compared to the best model-based filter.

中文翻译：

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一种用于机动目标跟踪和平滑的学习高斯过程方法

用于目标跟踪和平滑的基于模型的方法使用一组有限的模型来估计无限数量的可能目标轨迹。本文提出了一种数据驱动的方法，该方法使用无限数量函数的分布来表示可能的目标轨迹。开发了用于目标跟踪和平滑的递归高斯过程和基于导数的高斯过程方法，以及在线训练和参数学习。对两种高度机动场景的性能评估表明，与基于模型的最佳滤波器相比，所提出的方法分别在位置和速度估计方面提供了 80% 和 62% 的性能改进以及 49% 和 22%。