ArticleSensors (Basel, Switzerland)2021
Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- VQF-Based Decoupled Navigation Architecture for High-Curvature Maneuvering of Underwater Vehicles.Sensors (Basel, Switzerland) · 2026Article
- Multi-Source Information Fusion for Environmental Perception of Intelligent Vehicles Using Sage-Husa Adaptive Extended Kalman Filtering.Sensors (Basel, Switzerland) · 2025Article
- An Electro-Magnetic Log (EML) Integrated Navigation Algorithm Based on Hidden Markov Model (HMM) and Cross-Noise Linear Kalman Filter.Sensors (Basel, Switzerland) · 2025Article
- Enhancing Underwater SLAM Navigation and Perception: A Comprehensive Review of Deep Learning Integration.Sensors (Basel, Switzerland) · 2024Review
- Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with Kalman filter.Scientific reports · 2024Article
- A Method for Predicting Inertial Navigation System Positioning Errors Using a Back Propagation Neural Network Based on a Particle Swarm Optimization Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach.Sensors (Basel, Switzerland) · 2023Article
- Localization in Structured Environments with UWB Devices without Acceleration Measurements, and Velocity Estimation Using a Kalman-Bucy Filter.Sensors (Basel, Switzerland) · 2022Article
- The Key Technologies of Road Elevation Detection Based on Sensor Fusion.Sensors (Basel, Switzerland) · 2022Article
- Review of Underwater Sensing Technologies and Applications.Sensors (Basel, Switzerland) · 2021Review
- Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion.Sensors (Basel, Switzerland) · 2021Article
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
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Authors and funding
5 authors.
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Abstract
The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.
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