• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >Strong Tracking Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises
【24h】

Strong Tracking Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

机译：具有随机延迟测量和相关噪声的非线性系统的强跟踪滤波器

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper proposes a novel strong tracking filter (STF), which is suitable for dealing with the filtering problem of nonlinear systems when the following cases occur: that is, the constructed model does not match the actual system, the measurements have the one-step random delay, and the process and measurement noises are correlated at the same epoch. Firstly, a framework of decoupling filter (DF) based on equivalent model transformation is derived. Further, according to the framework of DF, a new extended Kalman filtering (EKF) algorithm via using first-order linearization approximation is developed. Secondly, the computational process of the suboptimal fading factor is derived on the basis of the extended orthogonality principle (EOP). Thirdly, the ultimate form of the proposed STF is obtained by introducing the suboptimal fading factor into the above EKF algorithm. The proposed STF can automatically tune the suboptimal fading factor on the basis of the residuals between available and predicted measurements and further the gain matrices of the proposed STF tune online to improve the filtering performance. Finally, the effectiveness of the proposed STF has been proved through numerical simulation experiments.
机译：本文提出了一种新型的强跟踪滤波器（STF），它适用于在以下情况发生时处理非线性系统的滤波问题：即，所构建的模型与实际系统不匹配，测量值只有一个步骤随机延迟，并且过程和测量噪声在同一时期相关。首先，推导了基于等效模型变换的去耦滤波器框架。此外，根据DF框架，开发了一种新的扩展卡尔曼滤波（EKF）算法，该算法采用一阶线性化近似。其次，基于扩展正交性原理（EOP），推导了次优衰落因子的计算过程。第三，通过将次优衰落因子引入上述EKF算法中来获得所提出的STF的最终形式。提出的STF可以根据可用和预测的测量之间的残差自动调谐次优衰落因子，并进一步对提出的STF的增益矩阵进行在线调谐以改善滤波性能。最后，通过数值模拟实验证明了所提STF的有效性。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2018年第2期|8052967.1-8052967.12|共12页
  • 作者

    Yang Hongtao; Meng Xinxin; Li Hui; Li Xiulan;

  • 作者单位

    Changchun Univ Technol, Coll Elect & Elect Engn, Changchun 130012, Jilin, Peoples R China;

    Changchun Univ Technol, Coll Elect & Elect Engn, Changchun 130012, Jilin, Peoples R China;

    Changchun Univ Technol, Coll Elect & Elect Engn, Changchun 130012, Jilin, Peoples R China;

    Changchun Univ Technol, Engn Training Ctr, Changchun 130012, Jilin, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号