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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28113

Title: An Adaptive Sensor Fusion Method with Applications in Integrated Navigation
Authors: Dah-Jing Jwo;Tsu-Pin Weng
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: Estimation and filtering;Mechanical and aerospace estimation
Date: 2008-07-06
Issue Date: 2011-10-21T02:35:55Z
Publisher: 17th IFAC World Congress
Abstract: Abstract:The Kalman filter (KF) is a form of optimal estimator characterized by recursive evaluation, which has been widely applied to the navigation sensor fusion. The adaptive algorithm is one of the approaches to prevent divergence problem of the Kalman filter when precise knowledge on the system models is not available.wo popular types of adaptive Kalman filter are the innovation-based adaptive estimation (IAE) approach and the adaptive fading Kalman filter (AFKF) approach. In this paper, an approach involving the concept of the two methods is proposed. The method is a synergy of the IAE and AFKF approaches. The ratio of the actual innovation covariance based on the sampled sequence to the theoretical innovation covariance is employed for dynamically tuning two filter parameters: fading factors and measurement noise scaling factors. The method has the merits of good computational efficiency and numerical stability. The matrices in the KF loop are able to remain positive definitive. Navigation sensor fusion using the proposed scheme applied to the loosely-coupled GPS/INS integration will be demonstrated.
Relation: 17, pp.9002-9007
URI: http://ntour.ntou.edu.tw/handle/987654321/28113
Appears in Collections:[通訊與導航工程學系] 演講及研討會

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