电子学报 ›› 2021, Vol. 49 ›› Issue (11): 2225-2233.DOI: 10.12263/DZXB.20201128

• 学术论文 • 上一篇    下一篇

跳变马尔可夫系统的最大混合相关熵状态估计

沈忱1,2, MIHAYLOVA Lyudmila2   

  1. 1.浙江工商大学信息与电子工程学院(萨塞克斯人工智能学院), 浙江 杭州 310018
    2.谢菲尔德大学自动控制与系统工程系, 谢菲尔德 S10 2TN
  • 收稿日期:2020-10-13 修回日期:2020-12-17 出版日期:2021-11-25 发布日期:2021-11-25
  • 作者简介:沈 忱(通信作者) 男,1988年1月出生于浙江宁波,现为浙江工商大学专任教师,主要研究方向为信息与信号处理、传感器网络技术等.E-mail:write2shen@sina.com
    Lyudmila Mihaylova 女,现为英国谢菲尔德大学信号处理与控制方向教授,主要研究方向为信息融合、序贯蒙特卡罗方法、智能自主系统、机器学习等.E-mail:l.s.mihaylova@sheffield.ac.uk
  • 基金资助:
    浙江省自然科学基金(LQ18F030003);国家留学基金(201908330102)

Maximum Mixture Correntropy State Estimation for Jump Markov Systems

Chen SHEN1,2, Lyudmila MIHAYLOVA2   

  1. 1.School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute),Zhejiang Gongshang University,Hangzhou,Zhejiang 310018,China
    2.Department of Automatic Control and Systems Engineering,University of Sheffield,Sheffield S10 2TN,UK
  • Received:2020-10-13 Revised:2020-12-17 Online:2021-11-25 Published:2021-11-25

摘要:

依托于多模型框架的跳变马尔可夫系统状态估计的性能通常受限于多模型间的信息融合程度.本文以交互式多模型方法为框架,针对跳变马尔可夫系统提出了一种基于最大混合相关熵的状态估计方法.为了能有效处理模型高阶信息,在混合和融合步骤引入最大混合相关熵测度替代常规的二阶统计矩准则,设计了关于系统状态的代价函数,通过最优化该函数得到状态估计的迭代解.仿真实验详尽展示了所提方法的主要特征,并表明其在高斯和非高斯噪声环境下都具有较好的估计效果.

关键词: 混合相关熵, 状态估计, 模型不确定

Abstract:

State estimation for jump Markov systems based on multiple models is usually influenced by the quality of model fusion. In this article, we propose a novel state estimation approach for the jump Markov systems based on the maximum mixture correntropy criterion (MMCC). The proposed approach is implemented within the framework of the interacting multiple models. To capture high order information from multiple models, we utilize the MMCC instead of second-order statistical measures at the mixing and fusion stages, respectively. Two cost functions with respect to the system state at different stages are designed and optimized to yield the resultant iterative solutions. Extensive simulated results present the feature of the proposed MMCC based approach, and prove its efficacy for both Gaussian and non-Gaussian cases.

Key words: mixture correntropy, state estimation, model uncertainty

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