NDLI: Alternative framework for the iterated unscented Kalman filter

Content Provider	IET Digital Library
Author	Chang, Guobin Xu, Tianhe Wang, Qianxin
Abstract	The iterated version of a family of non-linear Kalman filters, named the unscented transform (UT) based unscented Kalman filters (UKF), are revisited. Two existing frameworks of the iterated UKF are analysed and some shortcomings of them are pointed out. A new framework is proposed based on the statistical linear regression (SLR) perspective of the UT and the framework of the iterated extended Kalman filter (IEKF). The virtue of the proposed framework is twofold: first, the observation equation is linearised strictly following the SLR perspective implying that the regression error is also considered; second, it strictly follows the framework of the IEKF implying that in each iteration, the linearised equation is used to correct the a priori estimate rather than the latest estimate. A simple but illustrative benchmark example is simulated to check the feasibility of the proposed framework, and the results demonstrate the efficacy of the proposed framework.
Starting Page	258
Ending Page	264
Page Count	7
ISSN	17519675
Volume Number	11
e-ISSN	17519683
Issue Number	Issue 3, May (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/11/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2016.0214
Journal	IET Signal Processing
Publisher Date	2016-09-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	A Priori Estimate Alternative Framework Filtering Method in Signal Processing IEKF Interpolation And Function Approximation Iterated Extended Kalman Filter Iterated Unscented Kalman Filter Iterative Method Kalman Filter Linearised Equation Nonlinear Filter Nonlinear Kalman Filter Numerical Analysis Observation Equation Regression Analysis Regression Error Signal Processing Theory SLR Perspective Statistical Linear Regression Perspective Statistics Unscented Transform UT-based UKF
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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