arXiv 2004.049752020-04eess.SP

基于监督学习的在线跟踪过滤器：XGBoost实现

Supervised Learning Based Online Tracking Filters: An XGBoost Implementation

Jie Deng, Wei Yi

摘要：

目标状态滤波是传统目标跟踪框架中的一个重要模块。为了得到满意的跟踪结果，传统的贝叶斯方法通常需要精确的运动模型，这需要复杂的先验信息和参数估计。因此，建模过程对传统的贝叶斯目标跟踪滤波器有着关键的影响。然而，当遇到未知的先验信息或复杂的环境，传统的贝叶斯滤波器的精度大大降低的局限性。在本文中，我们提出了一个基于监督学习的在线跟踪滤波器（SLF）。首先，建立了一个完整的基于监督学习的跟踪滤波器框架，该框架直接基于数据驱动，建立了数据之间的映射关系。换句话说，所提出的滤波器不需要关于目标动态和杂波分布的先验信息。最后给出了一个基于XGBoost的实现，证明了SLF框架的可移植性和适用性。同时，该框架将鼓励其他研究人员继续扩大传统过滤器与监督学习相结合的领域。最后通过数值仿真实验验证了该滤波器的有效性。

英文摘要：

The target state filter is an important module in the traditional target tracking framework. In order to get satisfactory tracking results, traditional Bayesian methods usually need accurate motion models, which require the complicated prior information and parameter estimation. Therefore, the modeling process has a key impact on traditional Bayesian filters for target tracking. However, when encountering unknown prior information or the complicated environment, traditional Bayesian filters have the limitation of greatly reduced accuracy. In this paper, we propose a supervised learning based online tracking filter(SLF). First, a complete tracking filter framework based on supervised learning is established, which is directly based on data-driven and establishes the mapping relationship between data. In other words, the proposed filter does not require the prior information about target dynamics and clutter distribution. Then, an implementation based on eXtreme Gradient Boosting (XGBoost) is provided, which proves the portability and applicability of the SLF framework. Meanwhile, the proposed framework will encourage other researchers to continue to expand the field of combining traditional filters with supervised learning. Finally, numerical simulation experiments prove the effectiveness of the proposed filter.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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