A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

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In: Mathematical Problems in Engineering, 2015(2015), S. 1 - 12
Format: E-Artikel
Sprache: Unbestimmt
veröffentlicht: Hindawi Publishing Corporation
ISSN: 1024-123X
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finc.format ElectronicArticle
finc.mega_collection Hindawi Publishing Corporation (CrossRef)
finc.id ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTE1NS8yMDE1LzU4MTkwOQ
finc.source_id 49
ris.type EJOUR
rft.atitle A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System
rft.epage 12
rft.genre article
rft.issn 1024-123X
rft.jtitle Mathematical Problems in Engineering
rft.tpages 12
rft.pages 1-12
rft.pub Hindawi Publishing Corporation
rft.date 2015-01-01
x.date 2015-01-01T00:00:00Z
rft.spage 1
rft.volume 2015
abstract <jats:p>The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The evolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method.</jats:p>
authors Xu Xiaosu
Li Peijuan
Liu Jian-juan
doi 10.1155/2015/581909
languages und
url http://dx.doi.org/10.1155/2015/581909
version 0.9
x.subjects Engineering(all)
x.type journal-article
x.oa 1