• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on industrial informatics >An Improved Iterative FIR State Estimator and Its Applications
【24h】

An Improved Iterative FIR State Estimator and Its Applications

机译：改进的迭代FIR状态估计器及其应用

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

In this paper, an iterative finite impulse response (FIR) filter is proposed for discrete time-varying state-space models, with the purpose of a new initialization strategy for the iterative FIR structure as well as consideration of possible unexpected state dynamics in a finite horizon. A compensation variable that satisfies the Gaussian property is introduced into the state equation, and its probability density function (pdf) is estimated analytically together with the pdf of state variable using the variational Bayesian inference technique. Different from the existing methods, the proposed filter exploits the FIR structure from the perspective of pdf propagation, which provides a new efficient way to use the iterative FIR filtering structure without any particular initialization scheme. Moreover, the effects of uncertainties (caused by initialization and/or possible unmodeled state dynamics) on the filtering output are loosened adaptively. Two examples of applications demonstrate that the proposed algorithm can not only provide optimal estimates when the model used perfectly matches the measurements, but can also exhibit better robustness than the Kalman filter, optimal FIR filter, maximum likelihood FIR filter, and some commonly used robust and/or adaptive Kalman filters when the underlying process suffers from unpredicted uncertainties.
机译：本文提出了一种用于离散的时变状态模型的迭代有限脉冲响应（FIR）滤波器，目的是迭代FIR结构的新初始化策略以及在有限的情况下考虑可能的意外状态动态地平线。满足高斯性质的补偿变量被引入状态等式，并且使用变分贝叶斯推理技术与状态变量的PDF分析地估计其概率密度函数（PDF）。与现有方法不同，所提出的滤波器从PDF传播的角度挖掘FIR结构，这提供了一种在没有任何特定初始化方案的情况下使用迭代FIR滤波结构的新有效方法。此外，不确定地松开过滤输出对滤波输出的不确定性（由初始化和/或可能的初始化状态动态引起的）的影响。两个应用程序示例表明，当模型使用完全匹配测量时，所提出的算法不仅可以提供最佳估计，但也可以表现出比卡尔曼滤波器，最佳FIR滤波器，最大似然FIR滤波器更好的稳健性，以及一些常用的鲁棒/或自适应卡尔曼滤波器当潜在的过程受到不确定的不确定性时。

著录项

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