Qiang Guo, Long Teng, Tianxiang Yin, Yunfei Guo, Xinliang Wu, Wenming Song
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引用次数: 0
Abstract
The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and model-based algorithms. The time-varying constant velocity model is integrated into the Gaussian process (GP) of online learning to improve the performance of GP prediction. This integration is further combined with a generalized probabilistic data association algorithm to realize multi-target tracking. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.
对于杂乱环境中的高机动目标,现有机动目标跟踪方法的性能并不令人满意。本文利用数据驱动算法和基于模型算法的优势,提出了一种混合驱动方法,用于跟踪多个高机动目标。时变恒速模型被集成到在线学习的高斯过程(GP)中,以提高 GP 预测的性能。这种集成进一步与广义概率数据关联算法相结合,实现了多目标跟踪。通过仿真证明,与交互式多模型方法和数据驱动的 GP 运动跟踪器等广泛使用的算法相比,混合驱动方法的性能有显著提高。
期刊介绍:
Frontiers of Information Technology & Electronic Engineering (ISSN 2095-9184, monthly), formerly known as Journal of Zhejiang University SCIENCE C (Computers & Electronics) (2010-2014), is an international peer-reviewed journal launched by Chinese Academy of Engineering (CAE) and Zhejiang University, co-published by Springer & Zhejiang University Press. FITEE is aimed to publish the latest implementation of applications, principles, and algorithms in the broad area of Electrical and Electronic Engineering, including but not limited to Computer Science, Information Sciences, Control, Automation, Telecommunications. There are different types of articles for your choice, including research articles, review articles, science letters, perspective, new technical notes and methods, etc.