Open AccessAlternative framework for the iterated unscented Kalman filter
Author(s) -
Chang Guobin,
Xu Tianhe,
Wang Qianxin
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2016.0214
Subject(s) - iterated function , kalman filter , unscented transform , benchmark (surveying) , extended kalman filter , a priori and a posteriori , mathematics , computer science , algorithm , fast kalman filter , control theory (sociology) , mathematical optimization , artificial intelligence , statistics , mathematical analysis , philosophy , control (management) , geodesy , epistemology , geography
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.