Learning Target Dynamics While Tracking Using Gaussian Processes

Veiback Clas; Olofsson Jonatan; Lauknes Tom Rune; Hendeby Gustaf

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Learning Target Dynamics While Tracking Using Gaussian Processes

机译：使用高斯进程跟踪时学习目标动态

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摘要

Tracked targets often exhibit common behaviors due to influences from the surrounding environment, such as wind or obstacles, which are usually modeled as noise. Here, these influences are modeled using sparse Gaussian processes that are learned online together with the state inference using an extended Kalman filter. The method can also be applied to time-varying influences and identify simple dynamic systems. The method is evaluated with promising results in a simulation and a real-world application.

机译：由于周围环境的影响，如风或障碍，追踪的目标通常具有常见行为，例如风或障碍物，这通常是噪音。这里，使用稀疏高斯进程建模这些影响，该过程与使用扩展的卡尔曼滤波器一起在线学习。该方法也可以应用于时变的影响并识别简单的动态系统。在仿真和现实世界应用中，具有有前途的结果评估该方法。

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来源
《IEEE Transactions on Aerospace and Electronic Systems》 |2020年第4期|2591-2602|共12页
作者
Veiback Clas; Olofsson Jonatan; Lauknes Tom Rune; Hendeby Gustaf;
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作者单位

Linkoping Univ Dept Elect Engn S-58183 Linkoping Sweden;

Linkoping Univ Dept Elect Engn S-58183 Linkoping Sweden;

Norwegian Res Ctr N-9019 Tromso Norway;

Linkoping Univ Dept Elect Engn S-58183 Linkoping Sweden;

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原文格式 PDF
正文语种 eng
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关键词
Gaussian processes; Computational modeling; Adaptation models; Target tracking; Estimation; Time-varying systems; Extended Kalman filter (EKF); identification; online learning; sparse Gaussian process (GP); target tracking;

机译：高斯进程;计算建模;适应模型;目标跟踪;估计;时间变化系统;扩展卡尔曼滤波器（EKF）;识别;在线学习;稀疏高斯过程（GP）;目标跟踪;

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1. Transfer learning based on incorporating source knowledge using Gaussian process models for quick modeling of dynamic target processes [J] . Wang Kuanglei, Chen Junghui, Xie Lei, Chemometrics and Intelligent Laboratory Systems . 2020,第期

机译：基于结合源知识的基于使用高斯进程模型来转移学习，以便快速建模动态目标过程
2. Exploiting Differential Flatness for Robust Learning-Based Tracking Control Using Gaussian Processes [J] . Greeff Melissa, Schoellig Angela P. IEEE Control Systems Letters . 2021,第4期

机译：利用高斯进程利用基于鲁棒学习的跟踪控制的差分平整度
3. Distributed Recursive Gaussian Processes for RSS Map Applied to Target Tracking [J] . Feng Yin, Fredrik Gunnarsson Selected Topics in Signal Processing, IEEE Journal of . 2017,第3期

机译：RSS映射的分布式递归高斯过程应用于目标跟踪
4. Extended Target Tracking Using Gaussian Processes with High-Resolution Automotive Radar [C] . Kolja Thormann, Marcus Baum, Jens Honer International Conference on Information Fusion . 2018

机译：使用高斯过程和高分辨率汽车雷达的扩展目标跟踪
5. Study of multi-modal and non-Gaussian probability density functions in target tracking with applications to dim target tracking. [D] . Hlinomaz, Peter V. 2008

机译：研究目标跟踪中的多模态和非高斯概率密度函数及其在昏暗目标跟踪中的应用。
6. Extended Target Tracking and Feature Estimation for Optical Sensors Based on the Gaussian Process [O] . Haoyang Yu, Wei An, Ran Zhu 2019

机译：基于高斯过程的光学传感器扩展目标跟踪和特征估计
7. Learning Target Dynamics While Tracking Using Gaussian Processes [O] . Clas Veiback, Jonatan Olofsson, Tom Rune Lauknes, 2020

机译：使用高斯进程跟踪时学习目标动态

Learning Target Dynamics While Tracking Using Gaussian Processes

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