基于混合驱动高斯过程学习的强机动多目标跟踪方法

国强; 滕龙; 尹天祥; 郭云飞; 吴新良; 宋文明

doi:10.1631/FITEE.2300348

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基于混合驱动高斯过程学习的强机动多目标跟踪方法

常规文章 | Updated：2023-10-12

- 基于混合驱动高斯过程学习的强机动多目标跟踪方法
- Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking
- 信息与电子工程前沿（英文） 2023年24卷第11期页码：1647-1656
- Affiliations：
  
  1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  2.China National Aeronautical Radio Electronics Research Institute, Shanghai 200233, China
  3.College of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- Author bio：
  
  †E-mail: guoqiang@hrbeu.edu.cn
  ‡Corresponding author
  tianxiangyin@hdu.edu.cn
  gyf@hdu.edu.cn
- Funds：
  
  Technology Foundation for Basic Enhancement Plan, China(2021-JCJQ-JJ-0301);National Major Research and Development Project of China(2018YFE0206500);National Natural Science Foundation of China(62071140);International Scientific and Technological Cooperation Program of China(2015DFR10220)
- DOI：10.1631/FITEE.2300348
  中图分类号： TN953
- 收稿：2023-05-18，
  
  录用：2023-10-04，
  
  纸质出版：2023-11-0
- Accepted：
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国强, 滕龙, 尹天祥, 等. 基于混合驱动高斯过程学习的强机动多目标跟踪方法[J]. 信息与电子工程前沿（英文）, 2023,24(11):1647-1656.

Qiang GUO, Long TENG, Tianxiang YIN, et al. Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1647-1656.
国强, 滕龙, 尹天祥, 等. 基于混合驱动高斯过程学习的强机动多目标跟踪方法[J]. 信息与电子工程前沿（英文）, 2023,24(11):1647-1656. DOI： 10.1631/FITEE.2300348.

Qiang GUO, Long TENG, Tianxiang YIN, et al. Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1647-1656. DOI： 10.1631/FITEE.2300348.

摘要

现有机动目标跟踪方法在杂波环境中强机动目标的跟踪性能并不令人满意。本文提出一种混合驱动方法，利用数据驱动和基于模型算法的优点跟踪多个高机动目标。将时变恒速（CV）模型集成到在线学习的高斯过程（GP）中，提高高斯过程的预测性能。进一步与广义概率数据关联（GPDA）算法相结合，实现多目标跟踪。通过仿真实验可知，与广泛使用的机动目标跟踪算法如交互式多模型（IMM）和数据驱动的高斯过程运动跟踪器（GPMT）相比，提出的混合驱动方法具有显著的性能优势。

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.

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references

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