Adaptive Fading Kalman Filter with Q-adaptation for estimation of AUV dynamics

Chingiz Hajiyev*, S. Yenal Vural, Ulviyya Hajiyeva

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

This article is basically focused on application of the Robust Kalman Filter (RKF) algorithm to the estimation of high speed an autonomous underwater vehicle (AUV) dynamics. In the normal operation conditions of AUV, conventional Kalman filter gives sufficiently good estimation results. However, if any kind of malfunction occurs in the system, KF gives inaccurate results and diverges by time. This study, introduces Adaptive Fading Kalman Filter (AFKF) algorithm with the filter gain correction for the case of system malfunctions. By the use of defined variables named as single and multiple fading factors, the estimations are corrected without affecting the characteristic of the accurate ones.

Original languageEnglish
Title of host publication2012 20th Mediterranean Conference on Control and Automation, MED 2012 - Conference Proceedings
Pages697-702
Number of pages6
DOIs
Publication statusPublished - 2012
Event2012 20th Mediterranean Conference on Control and Automation, MED 2012 - Barcelona, Spain
Duration: 3 Jul 20126 Jul 2012

Publication series

Name2012 20th Mediterranean Conference on Control and Automation, MED 2012 - Conference Proceedings

Conference

Conference2012 20th Mediterranean Conference on Control and Automation, MED 2012
Country/TerritorySpain
CityBarcelona
Period3/07/126/07/12

Fingerprint

Dive into the research topics of 'Adaptive Fading Kalman Filter with Q-adaptation for estimation of AUV dynamics'. Together they form a unique fingerprint.

Cite this