Cart (Loading....) | Create Account
Close category search window
 

Generalized Multiple-Model Adaptive Estimation using an Autocorrelation Approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Alsuwaidan, B.N. ; Nat. Satellite Technol. Program, King Abdulaziz City for Sci. & Technol., Riyadh, Saudi Arabia ; Crassidis, J.L. ; Yang Cheng

In this paper a generalized multiple-model adaptive estimator (GMMAE) is presented that can be used to estimate unknown model and/or filter parameters, such as the noise statistics in filter designs. The main goal of this work is to provide an increased convergence rate for the estimated model parameters over the traditional multiple-model adaptive estimator (MMAE). Parameter elements generated from a quasi-random sequence are used to drive multiple parallel filters for state estimation. The current approach focuses on estimating the process noise covariance by sequentially updating weights associated with the quasi-random elements through the calculation of the likelihood function of the measurement-minus-estimate residuals, which also incorporates correlations between various measurement times. For linear Gaussian measurement processes the likelihood function is easily determined. A proof is provided that shows the convergence properties of the generalized approach versus the standard MMAE. Simulation results, involving a two-dimensional target tracking problem and a single-axis attitude problem, indicate that the new approach provides better convergence properties over a traditional multiple-model approach.

Published in:

Aerospace and Electronic Systems, IEEE Transactions on  (Volume:47 ,  Issue: 3 )

Date of Publication:

July 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.