Prepare Ships - PREdicted Positioning based on Egnss for SHIPS

a { text-decoration: none; color: #464feb; } tr th, tr td { border: 1px solid #e6e6e6; } tr th { background-color: #f5f5f5; }

PREParE SHIPS

Project Overview

The objective of the PREParE SHIPS project was to develop and demonstrate a collaborative and resilient navigation solution for maritime operations. The project enhanced existing navigation software by leveraging the unique capabilities of Galileo satellite navigation signals in combination with nautical information, onboard and external sensor technologies, and operational data.

The resulting navigation decision-support system enabled the collaborative exchange of dynamically predicted vessel positions through ship-to-ship communication based on resilient positioning information derived from Galileo receivers. This approach significantly improved navigational safety and operational efficiency while laying the foundation for future autonomous maritime operations.

In addition to using onboard vessel sensors, PREParE SHIPS employed machine learning techniques and historical vessel behaviour data to predict near-future vessel positions. These predictions were shared with nearby vessels and Vessel Traffic Services (VTS) centres to enhance situational awareness, support decision-making, and improve maritime safety.

To establish the requirements for the integrated positioning solution, the project defined and developed a collaborative automated vessel application. This application relied on a highly available positioning solution and integrated multiple navigational subsystems with ship-to-ship and ship-to-shore communication capabilities. Information received from connected vessels was aggregated to provide a more comprehensive operational picture.

Recognising that maritime transport was undergoing a gradual transition towards greater connectivity and automation, the project focused on solutions capable of delivering significant benefits even at low levels of technology adoption. PREParE SHIPS implemented and demonstrated high-precision fairway geo-fencing functionality that incorporated environmental data sources, such as wind and currents, together with traffic monitoring and predicted vessel movements. This enabled safer navigation decisions based on robust and reliable information. The project also implemented perception-layer sensor fusion solutions that utilised historical operational data and machine learning-based hybrid models to improve situational awareness and decision support.

Objectives

The PREParE SHIPS consortium integrated a high-precision positioning solution based on the capabilities of Galileo and other European GNSS signals into merchant shipping applications. The objective was to enable vessels to plan and conduct safe and increasingly automated voyages, particularly in complex traffic situations and challenging fairways.

Challenges Addressed

The project addressed key challenges related to improving:

• Maritime safety
• Energy efficiency
• Cybersecurity and operational resilience

These challenges were particularly relevant in a sector experiencing increasing levels of automation and greater dependence on safety-critical digital systems.

Solution and Results

Together with its project partners, RISE developed and validated an advanced positioning and navigation solution. Existing software platforms were enhanced through the integration of Galileo-based positioning capabilities, sensor technologies, and maritime operational data from both internal and external sources.

The project developed a navigation decision-support system that included the following key components:

EGNSS Resilient Positioning

The system enabled the dynamic prediction of future vessel positions based on information exchanged through Galileo-enabled receivers and transmitters. This improved positioning resilience and supported enhanced maritime situational awareness.

Real-Time Dynamic Prediction

Machine learning methods were applied to historical vessel behaviour data to generate near-future position predictions. These predictions were exchanged with nearby vessels and VTS centres, improving safety and supporting more informed navigational decisions.

Ship-to-Ship and Ship-to-Shore Interaction

A collaborative vessel application was developed to support the integrated positioning solution. The application utilised highly available positioning data to connect navigational systems with ship-to-ship and ship-to-shore communication services. Information from connected vessels was aggregated using next-generation AIS and VDES technologies, providing a shared operational picture.

Geo-Fencing and Sensor Fusion

The project implemented and demonstrated high-precision fairway geo-fencing solutions that combined positioning data with environmental information, including wind and current conditions, traffic monitoring data, and predicted vessel movements. These capabilities supported safer navigation and route planning.

Furthermore, PREParE SHIPS developed perception-layer sensor fusion solutions that integrated historical operational data with machine learning-based hybrid models to enhance the reliability and robustness of navigation support systems.

Work Packages

The project activities were organised into eight technical and business-oriented work packages:

• WP1 – Definition of Concept, Use Cases and System Requirements
• WP2 – High-Accuracy Positioning
• WP3 – Dynamic Real-Time Prediction
• WP4 – Ship-to-Ship and Ship-to-Shore Communication
• WP5 – Navigation Decision Support Subsystem and Human-Machine Interface (HMI)
• WP6 – Testing and Demonstration
• WP7 – Analysis, Commercialisation and Impact Assessment
• WP8 – Project Coordination

RISE Contribution

RISE contributed to the development and validation of resilient positioning and navigation support solutions that combined satellite navigation, sensor fusion, machine learning, and collaborative vessel communication. The project demonstrated how advanced positioning technologies and data-driven decision support could improve maritime safety, operational efficiency, and readiness for future autonomous shipping applications.