系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (8): 2474-2482.doi: 10.12305/j.issn.1001-506X.2022.08.11

• 传感器与信号处理 • 上一篇    下一篇

基于量测转换序贯滤波的GMPHD机动目标跟踪

侯子林, 程婷*, 彭瀚   

  1. 电子科技大学信息与通信工程学院, 四川 成都 611731
  • 收稿日期:2021-05-28 出版日期:2022-08-01 发布日期:2022-08-24
  • 通讯作者: 程婷
  • 作者简介:侯子林 (1996—), 男, 硕士研究生, 主要研究方向为目标跟踪、资源管理|程婷 (1982—), 女, 副教授, 博士, 主要研究方向为数据融合、非线性滤波技术|彭瀚 (1994—), 男, 硕士研究生, 主要研究方向为数据融合、非线性滤波技术
  • 基金资助:
    国家自然基金(62001084)

GMPHD based on measurement conversion sequential filtering for maneuvering target tracking

Zilin HOU, Ting CHENG*, Han PENG   

  1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
  • Received:2021-05-28 Online:2022-08-01 Published:2022-08-24
  • Contact: Ting CHENG

摘要:

针对多普勒雷达杂波环境下的多机动目标跟踪, 提出了一种基于去相关无偏量测转换序贯滤波的多模型高斯概率假设密度算法。针对量测的非线性, 将位置量测进行无偏量测转换, 将多普勒量测进行去偏量测转换, 并通过序贯滤波方式提高跟踪精度。针对多目标的机动性, 在高斯混合概率假设密度(Gaussian mixture probability hypothesis density, GMPHD)中引入多模型思想对模型相关的高斯分量进行预测、更新处理。仿真结果显示, 所提算法可以在杂波环境中实现有效的机动多目标跟踪, 与无迹卡尔曼多模型GMPHD相比不仅跟踪精度提升了38.15%, 而且大大改善了算法效率; 与无迹卡尔曼最适高斯近似GMPHD相比, 在效率上有小幅度的增加, 且跟踪精度提升了36.47%。

关键词: 高斯混合概率假设密度, 多模型, 多目标跟踪, 机动目标跟踪, 非线性量测

Abstract:

For multiple maneuvering targets tracking by Doppler radar in clutter, a multiple model Gaussian probability hypothesis density algorithm based on decorrelated unbiased converted measurement sequential filter is proposed. For the nonlinearity of the measurements, the position measurements are converted to unbiased measurements, and the Doppler measurement is converted to debiased pseudo measurement, and the tracking accuracy is improved by sequential filtering. For the maneuverability of the target, the idea of multiple model is introduced into Gaussian mixture probability hypothesis density (GMPHD), where the Gaussian components related to the model are predicted and updated. Simulation results demonstrate that the proposed algorithm can achieve effective maneuvering multi-target tracking in clutter. Compared with unscented Kalman multiple model GMPHD, the tracking accuracy is increased by 38.15%, and the algorithm efficiency is greatly improved. Compared with unscented Kalman best-fitting Gaussian approximation GMPHD, the efficiency is slightly increased, and the tracking accuracy is improved by 36.47%.

Key words: Gaussian mixture probability hypothesis density, multiple model, multiple target tracking, maneuvering target tracking, nonlinear measurement

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