论文标题
高斯流程回归基于基于目标跟踪的动力学模型学习算法
A Gaussian Process Regression based Dynamical Models Learning Algorithm for Target Tracking
论文作者
论文摘要
由于目标动作的不可预测性，操纵目标跟踪是传感器系统的挑战性问题。本文提出了一种新的数据驱动方法，用于学习目标的动态运动模型。非参数高斯过程回归（GPR）用于学习目标的自然移动不变运动（NSIM）行为，该行为在翻译上不变，并且随着目标移动而不必不断更新。可以通过将训练数据监视区域的不同监视区域的目标应用于训练数据的不同监视区域内的目标，通过将其纳入粒子滤波器（PF）实现。通过与常用的相互作用多重模型（IMM）-PF方法进行比较，可以在不同的操作场景中评估我们所提出的方法的性能，并为多目标跟踪（MTT）高度操纵的情况提供了约90美元的$ $ $绩效改进。
Maneuvering target tracking is a challenging problem for sensor systems because of the unpredictability of the targets' motions. This paper proposes a novel data-driven method for learning the dynamical motion model of a target. Non-parametric Gaussian process regression (GPR) is used to learn a target's naturally shift invariant motion (NSIM) behavior, which is translationally invariant and does not need to be constantly updated as the target moves. The learned Gaussian processes (GPs) can be applied to track targets within different surveillance regions from the surveillance region of the training data by being incorporated into the particle filter (PF) implementation. The performance of our proposed approach is evaluated over different maneuvering scenarios by being compared with commonly used interacting multiple model (IMM)-PF methods and provides around $90\%$ performance improvement for a multi-target tracking (MTT) highly maneuvering scenario.
