Defence AI · Sensor Fusion

UK MOD: 95% Wave Detection with Pragmatic AI

The UK MOD needed to stabilize their fleet of self-driving boats in rough oceans. We delivered a scalable, IMU-based solution that detects waves with 95% accuracy and minimizes rocking by 90%, ensuring mission safety and accuracy.

Client
UK MOD
Outcome
95% Accuracy
Start your project
IMU wave detection visualization with directional bounding for autonomous boats
Detection Rate
95%
wave direction identified
95%
Wave detection
accurately identifying direction
90%
Rocking reduction
minimising vessel motion
Minutes
System setup
rapid deployment across fleet
100%
Flexible system
easily integrates new features
The Challenge

Mission safety required ocean-aware autonomous boats

The UK MOD operates a fleet of self-driving boats to reduce costs and automate tasks. However, ocean waves were severely impacting their performance. Traditional sensor-based approaches using high-resolution radars and cameras failed to provide reliable, scalable wave detection across the entire fleet.

Performance degradation
Ocean waves significantly affected the performance and stability of the UK MOD’s self-driving boat fleet.
Sensor limitations
Existing high-resolution radars and cameras were either unreliable or lacked the necessary resolution for consistent wave detection.
Rogue wave detection
The system needed to identify unpredictable rogue waves and main wave direction using the minimum number of sensors possible.
Scalability issues
Initial computer vision approaches using state-of-the-art models were not cost-effective or scalable to the entire fleet.
The Solution

Pragmatic AI — Scalable, IMU-based detection

Instead of chasing high-cost sensor technologies, we took a pragmatic approach. By leveraging low-cost IMU sensors and sophisticated model fitting techniques, we developed a system that identifies the main wave direction in minutes, allowing vessels to adjust their course and minimize rocking motion.

Iterative development process ensuring the final solution met the client's pragmatic needs

Transition from expensive CV models to cost-effective IMU (Inertial Measurement Unit) sensors

Sophisticated model fitting techniques to identify main wave direction with high accuracy

Optimised algorithms for real-time wave pattern analysis and course correction

Hardware-agnostic design allowing for rapid deployment across diverse vessel types

Modular architecture that supports seamless integration of future sensing technologies

The Results

Fleet-wide stability quantified — 90% rocking reduction

The deployed system didn't just meet accuracy targets — it redefined how the MOD manages autonomous vessel stability across their fleet. Safer, more accurate, and 100% flexible for future sensing needs.

95%
Main wave detection
The system identifies the primary wave direction within minutes of deployment, enabling proactive navigation.
90%
Rocking minimisation
By aligning vessels against the waves, the system significantly reduced rocking, making the boats safer and sensor readings more accurate.
Scalable
Fleet-wide deployment
The IMU-based approach proved highly scalable, allowing for the entire fleet to be equipped with minimal hardware costs.
100%
Flexible architecture
Designed with future-proofing in mind, the system easily accommodates new features and sensor integrations.
"Our pragmatic approach is 100% focused in delivering a successful project that actually works in the real world, rather than just chasing the latest AI models."
GI
Gradient Insight
Mission Delivery Team
Technology Stack
IMU Sensor FusionModel FittingWave Pattern AnalysisIterative AI DevelopmentReal-time OptimizationFleet-wide ScalabilityPragmatic Data ScienceVessel Stabilization
At a Glance
Client UK Ministry of Defence
Industry Maritime / Defence
Delivery Fleet-wide Wave Sensing
Accuracy 95% Detection Rate
Focus Pragmatic AI Solutions

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