咨询与建议

看过本文的还看了

相关文献

该作者的其他文献

文献详情 >Nonlinear Filtering With a Pol... 收藏

Nonlinear Filtering With a Polynomial Series of Gaussian Random Variables

与 Gaussian 随机的变量的一个多项式系列的非线性的过滤

作     者:Servadio, Simone Zanetti, Renato Jones, Brandon A. 

作者机构:Univ Texas Austin Austin TX 78712 USA 

出 版 物:《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》 (IEEE航空航天与电子系统汇刊)

年 卷 期:2021年第57卷第1期

页      面:647-658页

核心收录:

学科分类:0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0825[工学-航空宇航科学与技术] 

基  金:Air Force Office of Scientific Research [FA9550-18-1-0351] 

主  题:Taylor series Kalman filters Algebra Nonlinear dynamical systems Gaussian approximation Approximation algorithms Estimation Differential algebra (DA) nonlinear filtering polynomial update 

摘      要:Filters relying on the Gaussian approximation typically incorporate the measurement linearly, i.e., the value of the measurement is premultiplied by a matrix-valued gain in the state update. Nonlinear filters that relax the Gaussian assumption, on the other hand, typically approximate the distribution of the state with a finite sum of point masses or Gaussian distributions. In this work, the distribution of the state is approximated by a polynomial transformation of a Gaussian distribution, allowing for all moments, central and raw, to be rapidly computed in a closed form. Knowledge of the higher order moments is then employed to perform a polynomial measurement update, i.e., the value of the measurement enters the update function as a polynomial of arbitrary order. A filter employing a Gaussian approximation with linear update is, therefore, a special case of the proposed algorithm when both the order of the series and the order of the update are set to one: it reduces to the extended Kalman filter. At the cost of more computations, the new methodology guarantees performance better than the linear/Gaussian approach for nonlinear systems. This work employs monomial basis functions and Taylor series, developed in the differential algebra framework, but it is readily extendable to an orthogonal polynomial basis.

读者评论 与其他读者分享你的观点

用户名:未登录
我的评分