Bayesian-Inversion-Based Multisensor Fusion Localization Algorithm for AUV

Underwater positioning is a crucial technology for autonomous underwater vehicles (AUVs) to effectively carry out a range of underwater tasks. With an underwater environment that is complex and constantly changing, it can be challenging for a single positioning technology to keep up with long-range,...

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Veröffentlicht in:IEEE internet of things journal Jg. 12; H. 14; S. 28913 - 28924
Hauptverfasser: Zhao, Wanlong, Liu, Kaiyuan, Liu, Ruitong, Liu, Gongliang, Meng, Weixiao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 15.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Autonomous underwater vehicles
Bayes methods
Bayesian analysis
Bayesian inversion
Covariance matrix
Extended Kalman filter
extended Kalman filter (EKF)
Inertial navigation
Inertial sensing devices
Localization
Location awareness
Multisensor fusion
Navigation systems
Noise
Pollution measurement
Position measurement
Positioning
Sea measurements
Sensor phenomena and characterization
Sensors
Task complexity
underwater localization
Velocity measurement
ISSN:2327-4662, 2327-4662
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Zusammenfassung:Underwater positioning is a crucial technology for autonomous underwater vehicles (AUVs) to effectively carry out a range of underwater tasks. With an underwater environment that is complex and constantly changing, it can be challenging for a single positioning technology to keep up with long-range, multitarget, and high-precision applications. Solutions in the field of underwater positioning are continually evolving and improving. A multisensor fusion positioning scheme, which integrates multiple sensors, including the inertial navigation system (INS), long baseline (LBL), Doppler velocity log (DVL), conductivity temperature depth (CTD), and depth gauge (DG), can provide more accurate and reliable positioning. In this article, a Bayesian-inversion-based multisensor fusion localization (BIMFL) algorithm is proposed to enhance the performance of underwater fusion positioning. The proposed algorithm combines the strengths of multiple sensors and provides more precise location information. BIMFL uses the residual values of the measured data from various sensors as observations. Bayesian inversion effectively integrates prior information with observational information, both of which have their covariance matrices considered, hence determining the appropriate parameters of the fusing positioning system. Additionally, an update iterative process based on the extended Kalman filter (EKF) is adopted in BIMFL to refine positioning results. Simulation and experimental results have shown that BIMFL surpasses traditional single-sensor and EKF-based filtering methods.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3567527