An Adaptive Filtering Method for Cooperative Localization in Leader–Follower AUVs

In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader–follower AUVs, the outliers in the sensor observa...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 22; číslo 13; s. 5016
Hlavní autori:	Zhao, Lin, Dai, Hong-Yi, Lang, Lin, Zhang, Ming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 02.07.2022 MDPI
Predmet:	Accuracy adaptive filter Algorithms Autonomous underwater vehicles Communication Decomposition extended Kalman filter Fuzzy logic Localization measurement anomaly multi-AUV cooperative localization Navigation systems Sensors Velocity
ISSN:	1424-8220, 1424-8220
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Abstract	In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader–follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage–Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative localization of multi-AUVs. Firstly, the measurement anomalies were evaluated by calculating the Chi-square test statistics based on the innovation. The detection threshold was determined according to the confidence level of the Chi-square test, and the Chi-square test statistics exceeding the threshold were regarded as measurement abnormalities. When measurement anomalies occurred, the Sage–Husa adaptive extended Kalman filter algorithm was improved by suboptimal maximum a posterior estimation using weighted exponential fading memory, and the measurement noise covariance matrix was adjusted online. The numerical simulation of leader–follower multi-AUV cooperative localization verified the effectiveness of the improved SHAEKF and demonstrated that the average root mean square and the average standard deviation of the localization errors based on the improved SHAEKF were significantly reduced in the case of the presence of measurement abnormalities.
AbstractList	In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader–follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage–Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative localization of multi-AUVs. Firstly, the measurement anomalies were evaluated by calculating the Chi-square test statistics based on the innovation. The detection threshold was determined according to the confidence level of the Chi-square test, and the Chi-square test statistics exceeding the threshold were regarded as measurement abnormalities. When measurement anomalies occurred, the Sage–Husa adaptive extended Kalman filter algorithm was improved by suboptimal maximum a posterior estimation using weighted exponential fading memory, and the measurement noise covariance matrix was adjusted online. The numerical simulation of leader–follower multi-AUV cooperative localization verified the effectiveness of the improved SHAEKF and demonstrated that the average root mean square and the average standard deviation of the localization errors based on the improved SHAEKF were significantly reduced in the case of the presence of measurement abnormalities. In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader-follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage-Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative localization of multi-AUVs. Firstly, the measurement anomalies were evaluated by calculating the Chi-square test statistics based on the innovation. The detection threshold was determined according to the confidence level of the Chi-square test, and the Chi-square test statistics exceeding the threshold were regarded as measurement abnormalities. When measurement anomalies occurred, the Sage-Husa adaptive extended Kalman filter algorithm was improved by suboptimal maximum a posterior estimation using weighted exponential fading memory, and the measurement noise covariance matrix was adjusted online. The numerical simulation of leader-follower multi-AUV cooperative localization verified the effectiveness of the improved SHAEKF and demonstrated that the average root mean square and the average standard deviation of the localization errors based on the improved SHAEKF were significantly reduced in the case of the presence of measurement abnormalities.In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader-follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage-Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative localization of multi-AUVs. Firstly, the measurement anomalies were evaluated by calculating the Chi-square test statistics based on the innovation. The detection threshold was determined according to the confidence level of the Chi-square test, and the Chi-square test statistics exceeding the threshold were regarded as measurement abnormalities. When measurement anomalies occurred, the Sage-Husa adaptive extended Kalman filter algorithm was improved by suboptimal maximum a posterior estimation using weighted exponential fading memory, and the measurement noise covariance matrix was adjusted online. The numerical simulation of leader-follower multi-AUV cooperative localization verified the effectiveness of the improved SHAEKF and demonstrated that the average root mean square and the average standard deviation of the localization errors based on the improved SHAEKF were significantly reduced in the case of the presence of measurement abnormalities.
Author	Lang, Lin Zhao, Lin Dai, Hong-Yi Zhang, Ming
AuthorAffiliation	2 College of Science, National University of Defense Technology, Changsha 410073, China; daihongyi1@163.com 1 College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China; zhaolin689@163.com (L.Z.); langlin_8502@nudt.edu.cn (L.L.)
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Cites_doi	10.3390/s20010060 10.33012/2017.15136 10.1177/0278364910380760 10.1016/j.oceaneng.2022.110812 10.1016/j.oceaneng.2022.111258 10.1134/S2075108717010059 10.1063/5.0019305 10.1007/s00190-013-0690-8 10.1109/JSEN.2019.2941273 10.3846/aviation.2021.13420 10.1109/OCEANS-Bergen.2013.6608166 10.3390/s16071073 10.1016/j.ijleo.2019.01.100 10.1109/TRO.2006.878957 10.1016/j.conengprac.2003.11.008 10.1109/OCEANSAP.2016.7485592
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Copyright	2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022
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References	Mourikis (ref_14) 2006; 22 Govaers (ref_4) 2018; Volume 1 Faghihinia (ref_13) 2021; 25 Narasimhappa (ref_19) 2020; 20 Zhang (ref_8) 2022; 254 Loebis (ref_10) 2004; 12 Kebkal (ref_3) 2017; 8 Fallon (ref_1) 2010; 29 ref_2 ref_18 Li (ref_12) 2011; 37 Xu (ref_9) 2019; 27 Wang (ref_11) 2022; 248 ref_16 ref_15 Zha (ref_21) 2019; 184 Chang (ref_17) 2014; 88 Zhai (ref_20) 2020; 91 ref_5 Wang (ref_7) 2016; 24 ref_6
References_xml	– ident: ref_6 doi: 10.3390/s20010060 – ident: ref_16 doi: 10.33012/2017.15136 – volume: 37 start-page: 724 year: 2011 ident: ref_12 article-title: Localization Performance Analysis of Cooperative Navigation System for Multiple AUVs Based on Relative Position Measurements with a Single Leader publication-title: Acta Autom. Sin. – volume: 29 start-page: 1461 year: 2010 ident: ref_1 article-title: Cooperative AUV Navigation using a Single Maneuvering Surface Craft publication-title: Int. J. Robot. Res. doi: 10.1177/0278364910380760 – volume: 248 start-page: 110812 year: 2022 ident: ref_11 article-title: Design and experimental results of passive iUSBL for small AUV navigation publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2022.110812 – volume: Volume 1 start-page: 1 year: 2018 ident: ref_4 article-title: Introduction to Kalman filter and its applications publication-title: Introduction and Implementations of the Kalman Filter – volume: 254 start-page: 111258 year: 2022 ident: ref_8 article-title: Multiple model AUV navigation methodology with adaptivity and robustness publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2022.111258 – volume: 8 start-page: 80 year: 2017 ident: ref_3 article-title: AUV acoustic positioning methods publication-title: Gyroscopy Navig. doi: 10.1134/S2075108717010059 – volume: 24 start-page: 511 year: 2016 ident: ref_7 article-title: Adaptive interacting multiple model filter for AUV integrated navigation publication-title: J. Chin. Inert. Technol. – volume: 91 start-page: 104501 year: 2020 ident: ref_20 article-title: The robust residual-based adaptive estimation Kalman filter method for strap-down inertial and geomagnetic tightly integrated navigation system publication-title: Rev. Sci. Instrum. doi: 10.1063/5.0019305 – volume: 88 start-page: 391 year: 2014 ident: ref_17 article-title: Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion publication-title: J. Geod. doi: 10.1007/s00190-013-0690-8 – volume: 20 start-page: 250 year: 2020 ident: ref_19 article-title: MEMS-Based IMU Drift Minimization: Sage Husa Adaptive Robust Kalman Filtering publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2019.2941273 – ident: ref_15 – volume: 25 start-page: 10 year: 2021 ident: ref_13 article-title: Analytical Expression for Uncertainty Propagation of Aerial Cooperative Navigation publication-title: Aviation doi: 10.3846/aviation.2021.13420 – volume: 27 start-page: 440 year: 2019 ident: ref_9 article-title: A multi-AUV cooperative localization method based on adaptive neuro-fuzzy inference system publication-title: J. Chin. Inert. Technol. – ident: ref_2 doi: 10.1109/OCEANS-Bergen.2013.6608166 – ident: ref_18 doi: 10.3390/s16071073 – volume: 184 start-page: 165 year: 2019 ident: ref_21 article-title: An improved nonlinear filter based on adaptive fading factor applied in alignment of SINS publication-title: Optik doi: 10.1016/j.ijleo.2019.01.100 – volume: 22 start-page: 666 year: 2006 ident: ref_14 article-title: Performance analysis of multirobot Cooperative localization publication-title: IEEE Trans. Robot. doi: 10.1109/TRO.2006.878957 – volume: 12 start-page: 1531 year: 2004 ident: ref_10 article-title: Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system publication-title: Control Eng. Pract. doi: 10.1016/j.conengprac.2003.11.008 – ident: ref_5 doi: 10.1109/OCEANSAP.2016.7485592
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SubjectTerms	Accuracy adaptive filter Algorithms Autonomous underwater vehicles Communication Decomposition extended Kalman filter Fuzzy logic Localization measurement anomaly multi-AUV cooperative localization Navigation systems Sensors Velocity
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