Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

An enhanced linear Kalman filter (EnLKF) algorithm for parameter estimation of nonlinear rational models

Zhu, Q and Yu, DL and Zhao, D (2017) An enhanced linear Kalman filter (EnLKF) algorithm for parameter estimation of nonlinear rational models. International Journal of Systems Science, 48 (3). pp. 451-461. ISSN 0020-7721

[img]
Preview
Text
M:\My Documents\My Work\Academic\Published Papers\Zhu-Quan-2016-submitted.pdf - Accepted Version

Download (510kB) | Preview

Abstract

In this study, an enhanced Kalman Filter formulation for linear in the parameters models with inherent correlated errors is proposed to build up a new framework for nonlinear rational model parameter estimation. The mechanism of linear Kalman filter (LKF) with point data processing is adopted to develop a new recursive algorithm. The novelty of the enhanced linear Kalman filter (EnLKF in short and distinguished from extended Kalman filter (EKF)) is that it is not formulated from the routes of extended Kalman Filters (to approximate nonlinear models by linear approximation around operating points through Taylor expansion) and also it includes LKF as its subset while linear models have no correlated errors in regressor terms. No matter linear or nonlinear models in representing a system from measured data, it is very common to have correlated errors between measurement noise and regression terms, the EnLKF provides a general solution for unbiased model parameter estimation without extra cost to convert model structure. The associated convergence is analysed to provide a quantitative indicator for applications and reference for further research. Three simulated examples are selected to bench-test the performance of the algorithm. In addition, the style of conducting numerical simulation studies provides a user-friendly step by step procedure for the readers/users with interest in their ad hoc applications. It should be noted that this approach is fundamentally different from those using linearisation to approximate nonlinear models and then conduct state/parameter estimate.

Item Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Systems Science on 1st January 2017, available online: http://www.tandfonline.com/10.1080/00207721.2016.1186243
Uncontrolled Keywords: 08 Information And Computing Sciences, 09 Engineering, 01 Mathematical Sciences
Subjects: Q Science > QA Mathematics
Divisions: Electronics and Electrical Engineering
Publisher: Taylor & Francis
Related URLs:
Date Deposited: 09 Oct 2017 11:19
Last Modified: 09 Oct 2017 13:14
DOI or Identification number: 10.1080/00207721.2016.1186243
URI: http://researchonline.ljmu.ac.uk/id/eprint/7302

Actions (login required)

View Item View Item