Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment

Abstract The measurement noise covariance R plays a vital role in the Kalman filter (KF) algorithm. Traditionally, a constant R is obtained by experience or experiments. However, the KF cannot achieve optimal estimation using constant R under non‐Gaussian conditions. A robust strategy for adaptive e...

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Autores principales:	Bing Zhu, Ding Li, Zuohu Li, Hongyang He, Xing Li
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/43fefd99c1724a938b29120ec1298989
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spelling	oai:doaj.org-article:43fefd99c1724a938b29120ec12989892021-11-12T15:34:29ZRobust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment1751-87921751-878410.1049/rsn2.12148https://doaj.org/article/43fefd99c1724a938b29120ec12989892021-12-01T00:00:00Zhttps://doi.org/10.1049/rsn2.12148https://doaj.org/toc/1751-8784https://doaj.org/toc/1751-8792Abstract The measurement noise covariance R plays a vital role in the Kalman filter (KF) algorithm. Traditionally, a constant R is obtained by experience or experiments. However, the KF cannot achieve optimal estimation using constant R under non‐Gaussian conditions. A robust strategy for adaptive estimation of R is proposed to suppress the influence of non‐Gaussian noise on the in‐motion alignment performance of the Doppler velocity log (DVL) velocity‐aided strapdown inertial navigation system (SINS). Furthermore, an improved Sage–Husa robust adaptive KF algorithm (SHRAKF) based on the Mahalanobis distance (MD) algorithm is proposed to handle the outliers that frequently occur within the complicated underwater environment. The contributions of this work are twofold—the SHRAKF (1) designs a robust strategy to adaptively estimate R in real time and (2) further improves filtering robustness and adaptability with the MD algorithm, conditional on the DVL outputs being contaminated by non‐Gaussian noise. A semi‐physical simulation experiment for SINS/DVL in‐motion alignment based on the test data is carried out, and the experimental results show that the SHRAKF adaptively estimates R in real time and effectively suppresses observational outliers. For non‐Gaussian noise pollution, including outliers and heavy‐tailed noise, the SHRAKF performs better than traditional methods.Bing ZhuDing LiZuohu LiHongyang HeXing LiWileyarticleTelecommunicationTK5101-6720ENIET Radar, Sonar & Navigation, Vol 15, Iss 12, Pp 1583-1593 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Bing Zhu Ding Li Zuohu Li Hongyang He Xing Li Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment
description	Abstract The measurement noise covariance R plays a vital role in the Kalman filter (KF) algorithm. Traditionally, a constant R is obtained by experience or experiments. However, the KF cannot achieve optimal estimation using constant R under non‐Gaussian conditions. A robust strategy for adaptive estimation of R is proposed to suppress the influence of non‐Gaussian noise on the in‐motion alignment performance of the Doppler velocity log (DVL) velocity‐aided strapdown inertial navigation system (SINS). Furthermore, an improved Sage–Husa robust adaptive KF algorithm (SHRAKF) based on the Mahalanobis distance (MD) algorithm is proposed to handle the outliers that frequently occur within the complicated underwater environment. The contributions of this work are twofold—the SHRAKF (1) designs a robust strategy to adaptively estimate R in real time and (2) further improves filtering robustness and adaptability with the MD algorithm, conditional on the DVL outputs being contaminated by non‐Gaussian noise. A semi‐physical simulation experiment for SINS/DVL in‐motion alignment based on the test data is carried out, and the experimental results show that the SHRAKF adaptively estimates R in real time and effectively suppresses observational outliers. For non‐Gaussian noise pollution, including outliers and heavy‐tailed noise, the SHRAKF performs better than traditional methods.
format	article
author	Bing Zhu Ding Li Zuohu Li Hongyang He Xing Li
author_facet	Bing Zhu Ding Li Zuohu Li Hongyang He Xing Li
author_sort	Bing Zhu
title	Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment
title_short	Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment
title_full	Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment
title_fullStr	Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment
title_full_unstemmed	Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment
title_sort	robust adaptive kalman filter for strapdown inertial navigation system dynamic alignment
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/43fefd99c1724a938b29120ec1298989
work_keys_str_mv	AT bingzhu robustadaptivekalmanfilterforstrapdowninertialnavigationsystemdynamicalignment AT dingli robustadaptivekalmanfilterforstrapdowninertialnavigationsystemdynamicalignment AT zuohuli robustadaptivekalmanfilterforstrapdowninertialnavigationsystemdynamicalignment AT hongyanghe robustadaptivekalmanfilterforstrapdowninertialnavigationsystemdynamicalignment AT xingli robustadaptivekalmanfilterforstrapdowninertialnavigationsystemdynamicalignment
_version_	1718430366083055616

Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment

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