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Publikācija: Adaptive Extended Kalman Filter for Aided Inertial Navigation System

Publication Type Scientific article indexed in SCOPUS or WOS database
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language Adaptive Extended Kalman Filter for Aided Inertial Navigation System
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Vadims Bistrovs
Ansis Klūga
Abstract GPS and micro-electro-mechanical (MEMS) inertial systems have complementary qualities that make integrated navigation systems more robust. The effective integration of GPS and MEMS sensors is still challenging task for low cost navigation system. Kalman filters are widely used for sensor data fusion and navigation in mobile robotics. Taking in account GPS and inertial data processing problem nonlinearity, the Extended Kalman Filter (EKF) is used. One of the most important tasks in integration of GPS/INS is to choose the realistic dynamic model covariance matrix Q and measurement noise covariance matrix R for use in the Kalman filter. Adaptive algorithms automatically adjust Kalman filter system and measurement noise covariance matrix parameters taking in account navigation process performance i.e. position error. In this paper innovation based adaptive EKF for adapting R and Q was used in order to improve navigation system performance during GPS signal outages
DOI: 10.5755/j01.eee.122.6.1818
Hyperlink: http://www.eejournal.ktu.lt/index.php/elt/article/view/1818 
Reference Bistrovs, V., Klūga, A. Adaptive Extended Kalman Filter for Aided Inertial Navigation System. Electronics and Electrical Engineering, 2012, Vol.122, No.6, pp.37-40. e-ISSN 2029-5731. ISSN 1392-1215. Available from: doi:10.5755/j01.eee.122.6.1818
Additional information Citation count:
ID 17244