基于GPS的堆叠串行LSTM组合神经网络目标跟踪方法

作者

  • 金 学波 北京工商大学,计算机与人工智能学院,北京 100048 作者 https://orcid.org/0000-0002-2230-0077
  • 刘 嵩政 北京工商大学,计算机与人工智能学院,北京 100048 作者

DOI:

https://doi.org/10.52810/FAAI.2024.002

关键词:

轨迹估计, 循环神经网络, GPS, 滤波算法, LSTM, 堆叠串行结构

摘要

机动目标轨迹估计广泛应用于无人驾驶、拦截导弹等领域。由于机动目标的运动特性的不确定性、传感器精度低的问题,轨迹估计一直是一个开放研究问题和一项有挑战性的工作。本文提出了目标运动特性不确情况下,一种基于深度 LSTM 神经网络的轨迹估计方法。该网络有两个具有堆叠串行关系的 LSTM 网络组成,其中,一个 LSTM 网络用于预测运动状态,另一个网络用于更新状态。与经典的基于机动模型的 Kalman 滤波器相比,本文的方法基于网络学习、无需对运动特性和传感器特性进行建模。实验结果表明,该方法可以在目标运动具有未知和不确定性的情况下,有效提升轨迹的估计性能。

作者简历

  • 作者
    金学波  教授,博士生导师.1994年毕业于吉林大学(原吉林工业大学)获学士学位,1997年毕业于吉林大学(原吉林工业大学)获硕士学位,2004年获得浙江大学控制科学与工程博士学位,导师为孙优贤院士.研究方向为信息融合、模式识别与预测、大数据分析、深度学习等.近年来在相关领域主持了1项国家科技支撑计划课题、4项国家自然基金面上项目等多项研究课题.获2021年度中国粮油学会科学技术奖一等奖。在时序信号模式识别、图像目标检测与识别等研究领域,已发表SCI、EI收录等高水平学术论文159篇,其中7篇为ESI高被引论文(前1%)、3篇ESI热点论文(前0.1%),已授权国家发明专利20余项,出版关于传感器信号识别与状态估计、多传感器信息融合的学术专著3部.担任SCI收录期刊Sensors编委,为IEEE/CAA Journal of Automatica Sinica、Knowledge-Based Systems等中科院一区SCI期刊审稿人.
  • 作者
    刘嵩政, 2024年毕业于北京工商大学电子信息专业,获硕士学位。研究方向为模式识别与信息融合、卡尔曼滤波、目标跟踪、深度学习等。

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2024-04-18

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学波金., & 嵩政刘. (2024). 基于GPS的堆叠串行LSTM组合神经网络目标跟踪方法. 人工智能前沿与应用, 1(1), 16-31. https://doi.org/10.52810/FAAI.2024.002