WP 2 Innovation leads
Digital Twin for Maritime Communication System Performance Prediction
Researcher/ Supervisor: Manju James/ Kimmo Kansanen
Problem: Maritime communication systems are subject to performance degradation and reliability due to fluctuating sea conditions and weather. Radio wave propagation in maritime environment is affected by sea state, ship movements, and sea surface scattering and reflection. Deterministic channel modelling will be computationally intense in maritime environment with deep fading phenomena. This makes the channel performance prediction difficult.
Solution: A model of a maritime communication channel with dense multipath components from the ocean surface using machine learning algorithms. The work will use experimental and synthetic channel data to train the algorithm for channel performance prediction and validate the model with experimental data and existing research literature.
Purpose & Value: The result can be utilized to support graceful maritime communication link maintenance by the communication service providers in marine environment. The solution can help in decision-support for communication channel selection in situations of degraded maritime communication channel mainly due to local weather and other unpredictable events.
Radio Channel measurements and modelling
Researcher/ Supervisor: Giacomo Melloni/ Torbjörn Ekman
Problem: The demand for reliable, high-speed radio communications at sea is increasing. Key factors affecting maritime channels are Line-OfSight, specular reflection, and diffuse scattering. Interestingly, calm seas can worsen specular reflection, reducing signal quality. There's limited research on how sea surface scattering at high frequencies impacts ship-to-shore communications.
Solution: New channel models for maritime communications to enable reliable and secure data transfer among ships, the Remote Control Centre (RCC), and other marine traffic, tailored to operational needs.
Purpose & Value: The new models can be embedded in on-board devices to emulate the channel and adapt the link to maximize available resources. This can enable reduction of outage probability and enhancing reliability and capacity of the communication.
Data fusion in maritime IoT
Researcher/ Supervisor: Peter Morris/ Pierluigi Salvo Rossi
Problem: Autonomous vessel development is complex due to vast environmental variables, integrated system dependencies, and costly, hard-to-reproduce sea tests. Ensuring safe failure handling is difficult, while growing interest demands exploring coordination and collaboration capabilities across numerous challenging scenarios.
Solution: A configurable simulator models sensors, failures, environments, and vessel systems to enable controlled, repeatable testing of autonomous vessel behaviour. It supports realistic noise, fault propagation analysis, and multi-vessel scenarios for evaluating performance, situational awareness (SA), and collision-avoidance (CA) systems.
Purpose & Value: The simulator enables systematic evaluation of SA and CA systems across complex scenarios, revealing impacts of noise and failures and supporting third-party validation. It also tests higher-level coordination, assessing robustness in mixed cooperative and adversarial vessel environments.
Ship-shore radar network
Researcher/Supervisor: Lukas Herrmann/ Egil Eide
Problem: Situational awareness require that a vessel must possess comprehensive situational information and understanding. This becomes particularly challenging in harbors and close-to-shore areas since they are busier, less predictable, and may include vessels such as small boats or kayaks. The vessel sensor suit may not be sufficient to ensure this under all operating conditions.
Solution: A conceptual solution based on shorebased radar network is developed, consisting of use of several radars at multiple sites which is capable of detecting vessels within the observed area and distributing the position information of all the tracked target.
Purpose & Value: The solution will provide an alternative to tackle the challenges of the detection and tracking of targets in low signal-to-noise ratio maritime scenarios including considerations regarding real-time capabilities, hence increasing operational safety.
A Decision Support System for Autonomous Ship Trajectory Planning
Researcher/ Supervisor: Melih Akdağ/ Tor Arne Johansen
Problem: When planning the trajectory for an autonomous ship many variables must be included and integrated in the decision of selection of trajectory.
Solution: A Decision support solution with human–machine collaboration by utilizing multiobjective optimization algorithms to find multiple alternative route solutions meeting the objective criteria and ranking the solutions by using the human operator preferences for each objective.
Purpose & Value: The solution may be integrated in into digital chart solutions at a Remote operation Center or deployed as independent decision support system.
