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A Learning Gaussian Process Approach for Maneuvering Target Tracking and Smoothing

机译:用于机动目标跟踪和平滑的学习高斯工艺方法

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摘要

Model-based approaches for target tracking and smoothing estimate the infinite number of possible target trajectories using a finite set of models. This article 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.
机译:基于模型的目标跟踪方法和平滑估计使用有限组模型的无限数量的可能的目标轨迹。本文提出了一种数据驱动方法,它代表了使用无限数量函数的分布的可能的目标轨迹。递归高斯过程,以及基于衍生的目标跟踪过程方法,并开发了在线培训和参数学习的平滑。与最佳模型的滤波器相比,两个高机动方案的性能评估表明,所提出的方法分别在速度估计中提供80%和62%的性能提高,49和22%。

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