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NEURONAV: AI-Enhanced Maritime Navigation System
发布时间：
2025-08-21 13:15
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NEURONAV is an advanced AI-enhanced positioning system designed specifically for maritime navigation, aiming to improve the positioning accuracy of Global Navigation Satellite Systems (GNSS) in complex environments.
The system was jointly developed by Romanian engineering company InSpace (RISE) and the Marine Hydrological Department (MHD), funded by the European Space Agency's NAVISP Element 2 project, primarily addressing key maritime navigation issues such as multipath errors, signal interference, and limitations in ground truth accuracy.
The system's development is based on experience accumulated from multiple European Space Agency projects, including large-scale data collection activities conducted in the Black Sea, Aegean Sea, and Danube River basin. These operations deployed equipment and dedicated vessels to collect and analyze GNSS data for performance evaluation, interference detection, and multipath effect characterization.
At a recent event hosted by the European Space Agency, Sergiu-Stefan Mihai and Ileana Mihu from RISE presented the project's final results. Mihai stated that the core of NEURONAV is a machine learning model, specifically a convolutional neural network (CNN), used to predict and correct position errors caused by multipath effects.
Unique Data Processing Method
The system first collects GNSS-derived parameters, including satellite azimuth, elevation angle, carrier-to-noise ratio (C/N₀), and pseudorange residuals. These inputs are transformed into Cartesian error space representations, serving as input matrices for the CNN. The network processes this data through convolutional layers, max pooling, and fully connected regression layers to predict position corrections for X, Y, and Z coordinates.
NEURONAV hardware uses an integrated Septentrio mosaic-X5 multi-constellation multi-frequency GNSS receiver, equipped with a Jetson Nano single-board computer for AI computation. Data collection, processing, and storage are fully automated, enabling continuous model training and validation without human intervention.
In testing activities conducted in the Black Sea using MHD's hydrological survey vessel, multiple trials showed stable results. Even under strong radio frequency interference conditions intensified by GNSS jamming and spoofing following the outbreak of the conflict in Ukraine, the trials demonstrated measurable accuracy improvements. For example, in one validation, the average position error decreased from the receiver's raw output of 1.507 meters to 1.268 meters after NEURONAV correction, with the 95th percentile error improving by over 25% compared to uncorrected GNSS data.
NEURONAV can enhance the performance of existing GNSS receivers and ensure security and reliability through local data processing, offering potential benefits to both professional and recreational maritime users. Mihai stated that future work will focus on improving system performance under strong interference, expanding the dataset, and integrating more navigation sensors to enhance robustness.
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NEURONAV: AI-Enhanced Maritime Navigation System
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