A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques

In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wenqi Wu [verfasserIn] Liangqing Lu [verfasserIn] Jinling Wang [verfasserIn] Wanli Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	DVL-aided in-motion alignment AUKF measurement noise covariance

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 13(2013), 1, Seite 1046-1063
Übergeordnetes Werk:	volume:13 ; year:2013 ; number:1 ; pages:1046-1063

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s130101046

Katalog-ID:	DOAJ031676812

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source	In Sensors 13(2013), 1, Seite 1046-1063 volume:13 year:2013 number:1 pages:1046-1063
sourceStr	In Sensors 13(2013), 1, Seite 1046-1063 volume:13 year:2013 number:1 pages:1046-1063
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institution	findex.gbv.de
topic_facet	DVL-aided in-motion alignment AUKF measurement noise covariance Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Wenqi Wu @@aut@@ Liangqing Lu @@aut@@ Jinling Wang @@aut@@ Wanli Li @@aut@@
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callnumber-first	T - Technology
author	Wenqi Wu
spellingShingle	Wenqi Wu misc TP1-1185 misc DVL-aided misc in-motion alignment misc AUKF misc measurement noise covariance misc Chemical technology A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques
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topic_title	TP1-1185 A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques DVL-aided in-motion alignment AUKF measurement noise covariance
topic	misc TP1-1185 misc DVL-aided misc in-motion alignment misc AUKF misc measurement noise covariance misc Chemical technology
topic_unstemmed	misc TP1-1185 misc DVL-aided misc in-motion alignment misc AUKF misc measurement noise covariance misc Chemical technology
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title	A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques
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title_full	A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques
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title_sort	novel scheme for dvl-aided sins in-motion alignment using ukf techniques
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title_auth	A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques
abstract	In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS error model is presented and the measurement model is derived under the assumption that large misalignments may exist. Since a priori knowledge of the measurement noise covariance is of great importance to robustness of the UKF, the covariance-matching methods widely used in the Adaptive KF (AKF) are extended for use in Adaptive UKF (AUKF). Experimental results show that the proposed DVL-aided alignment model is effective with any initial heading errors. The performances of the adaptive filtering methods are evaluated with regards to their parameter estimation stability. Furthermore, it is clearly shown that the measurement noise covariance can be estimated reliably by the adaptive UKF methods and hence improve the performance of the alignment.
abstractGer	In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS error model is presented and the measurement model is derived under the assumption that large misalignments may exist. Since a priori knowledge of the measurement noise covariance is of great importance to robustness of the UKF, the covariance-matching methods widely used in the Adaptive KF (AKF) are extended for use in Adaptive UKF (AUKF). Experimental results show that the proposed DVL-aided alignment model is effective with any initial heading errors. The performances of the adaptive filtering methods are evaluated with regards to their parameter estimation stability. Furthermore, it is clearly shown that the measurement noise covariance can be estimated reliably by the adaptive UKF methods and hence improve the performance of the alignment.
abstract_unstemmed	In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS error model is presented and the measurement model is derived under the assumption that large misalignments may exist. Since a priori knowledge of the measurement noise covariance is of great importance to robustness of the UKF, the covariance-matching methods widely used in the Adaptive KF (AKF) are extended for use in Adaptive UKF (AUKF). Experimental results show that the proposed DVL-aided alignment model is effective with any initial heading errors. The performances of the adaptive filtering methods are evaluated with regards to their parameter estimation stability. Furthermore, it is clearly shown that the measurement noise covariance can be estimated reliably by the adaptive UKF methods and hence improve the performance of the alignment.
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title_short	A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques
url	https://doi.org/10.3390/s130101046 https://doaj.org/article/15e3ad29b22e4eb0ba746352c8958e6a http://www.mdpi.com/1424-8220/13/1/1046 https://doaj.org/toc/1424-8220
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