Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (11): 3127-3132.DOI: 10.11772/j.issn.1001-9081.2020030402

• Artificial intelligence • Previous Articles     Next Articles

Particle flow filter algorithm based on “innovation error”

ZHOU Deyun1, LIU Bin1,2, SU Qian1   

  1. 1. Engineering Research Center for Metallurgical Automation and Measurement Technology, Ministry of Education(Wuhan University of Science and Technology), Wuhan Hubei 430081, China;
    2. Hubei Province Key Laboratory of Systems Science in Metallurgical Process(Wuhan University of Science and Technology), Wuhan Hubei 430081, China
  • Received:2020-04-02 Revised:2020-04-29 Online:2020-11-10 Published:2020-06-04
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61903281).

基于“新息误差”的粒子流滤波算法

周德运1, 刘斌1,2, 苏茜1   

  1. 1. 冶金自动化与测量技术教育部工程研究中心(武汉科技大学), 武汉 430081;
    2. 冶金工业过程系统科学湖北省重点实验室(武汉科技大学), 武汉 430081
  • 通讯作者: 刘斌(1972-),女,吉林珲春人,教授,博士,主要研究方向:复杂系统建模、模式识别、智能系统、网络控制;liubin@wust.edu.cn
  • 作者简介:周德运(1994-),男,湖北咸宁人,硕士研究生,主要研究方向:状态估计、移动机器人实时定位与建图;苏茜(1987-),女,湖北荆州人,讲师,博士,主要研究方向:多相流测试、故障诊断、信息处理
  • 基金资助:
    国家自然科学基金资助项目(61903281)。

Abstract: There exist some problems in the process of Particle Filter (PF), such as particle weight degeneracy, curse of dimensionality, and high computational cost. By constructing a logarithmic homotopy function, particle flow filter can avoid the problem of particle weight degeneracy, but it relies on the observation equation too much when solving the boundary value problem, and has poor effect when the noise is high. To address these problems, an improved particle flow filter algorithm was proposed. Firstly, an "innovation error" structure was introduced into the process of particle flow, so that the update of each particle is independent. Then, the Galerkin finite element method was utilized to obtain the numerical solution of the boundary value problem, so as to avoid the numerical instability problem that may be caused by the fitting sample prior. Finally, the performance of the improved algorithm was tested in the common nonlinear filter model and the maneuvering target tracking model. Simulation results show that the improved algorithm can suppress the dependency of the system on observation information, and has relatively good results with increasing noise, which effectively improve the filtering accuracy, and in multi-dimensional target tracking cases, the algorithm's computational efficiency and filtering accuracy are higher than those of the standard particle filter.

Key words: particle filter, weight degeneracy, particle flow filter, innovation error, Galerkin finite element method

摘要: 在粒子滤波(PF)过程中存在粒子权值退化、维度灾难、计算成本高等问题。粒子流滤波通过构造对数同伦函数避免了粒子权值退化问题,但是在求解边值问题时过于依赖观测方程,当噪声较大时效果较差。针对上述问题,提出了一种改进的粒子流滤波算法。首先,该算法在粒子流动的过程中引入了一种“新息误差”结构,使每个粒子的更新相互独立;其次,利用Galerkin有限元法求得边值问题的数值解,从而消除了拟合样本先验可能导致的数值不稳定问题;最后,分别在通用非线性滤波模型和机动目标跟踪模型中对改进的算法进行了性能测试。仿真结果表明,改进的算法可以抑制系统对观测信息的依赖性,在噪声增大的情况下也能得到相对较好的结果,有效改善了滤波精度,而在多维目标跟踪情况下算法的计算效率与滤波精度高于标准粒子滤波。

关键词: 粒子滤波, 权值退化, 粒子流滤波, 新息误差, Galerkin有限元法

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