Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform

Calabuig, Neus Revert; Laarossi, Ismail; González, Antonio Álvarez; Nuñez, Alejandro Pérez; Pérez, Laura González; Casas, Abraham

doi:10.3390/s23020925

Cited by 3 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to provide reliable localization information, simultaneous localization and map construction (SLAM) systems can be used, as well as multi−sensor integrated systems that combine LiDAR [ 40 ] and navigation systems to solve problems caused by GNSS antenna failures. To reduce GNSS and Inertial Measurement Unit (IMU) fusion costs and achieve desired accuracy, different navigation satellite [ 41 , 42 , 43 ] constellations need to be combined, especially in urban working environments.…”

Section: Introductionmentioning

confidence: 99%

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

Yin

Zhang

Guo

et al. 2023

Sensors

View full text Add to dashboard Cite

High−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch–Tung–Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF−RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF−RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF−RTS algorithm is more robust and provides more accurate localization.

show abstract

Section: Introductionmentioning

confidence: 99%

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

Yin

Zhang

Guo

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Sustainable IoT-Based Data Logger System for Marine Applications Using Renewable Energy

Faizabadi,

Adnan,

Norazaruddin

et al. 2023

2023 IEEE 8th International Conference on Engineering Technologies and Applied Sciences (ICETAS)

View full text Add to dashboard Cite

Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical Stress

Melgarejo,

Pérez,

Ruiz

et al. 2024

Sensors

View full text Add to dashboard Cite

This study presents the design and validation of a numerical method based on an AI-driven ROM framework for implementing stress virtual sensing. By leveraging Reduced-Order Models (ROMs), the research aims to develop a virtual stress transducer capable of the real-time monitoring of mechanical stresses in mechanical components previously analyzed with high-resolution FEM simulations under a wide range of multiple load scenarios. The ROM is constructed through neural networks trained on Finite Element Method (FEM) outputs from multiple scenarios, resulting in a simplified yet highly accurate model that can be easily implemented digitally. The ANN model achieves a prediction error of MAEtest=(0.04±0.06) MPa for the instantaneous mechanical stress predictions, evaluated over the entire range of stress values (0 to 5.32 MPa) across the component structure. The virtual sensor is capable of producing a quasi-instantaneous, detailed full stress map of the component in just 0.13 s using the ROM, for any combination of 4-load inputs, compared to the 6 min and 31 s required by the FEM. Thus, the approach significantly reduces computational complexity while maintaining a high degree of precision, enabling efficient real-time monitoring. The proposed method’s effectiveness is demonstrated through rigorous ROM validation, underscoring its potential for stress control. This precise AI-driven procedure opens new horizons for predictive maintenance strategies centered on stress cycle monitoring.

show abstract

Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform

Cited by 3 publications

References 26 publications

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

Sustainable IoT-Based Data Logger System for Marine Applications Using Renewable Energy

Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical Stress

Contact Info

Product

Resources

About