The UK’s coastal waters are monitored by a wide range of maritime sensors, including radar and Automatic Identification Systems (AIS). These systems feed live track data into operational displays. But with one vessel often generating multiple uncorrelated tracks from multiple sensors, the result is a cluttered and confusing picture.
Operators at the Joint Maritime Security Centre (JMSC) need a correlation capability to reduce this complexity, combining sensor inputs into a single, coherent track per vessel. This would improve decision-making, reduce fatigue and support maritime domain awareness.
Initially, the customer approached the Accelerated Capability Environment (ACE) with a request to manage an existing supplier for Phase 2 trials. Instead, ACE proposed a more open and exploratory approach: using more rapid trials to understand the capability landscape and shape a Statement of Requirement (SoR) before any formal procurement begins.
The challenge
The original assumption was that a “monolithic” off-the-shelf correlation engine could be procured. However, without performing trials to understand the scope and the art of the possible within the marketplace, there was a risk that a SoR would not be sufficiently informed to enable JMSC to effectively procure an optimum solution.
ACE’s challenge was to trial multiple solutions in a neutral, secure environment and use real historic data to explore what worked, what didn’t and what other system factors influence correlation. ACE also helped the customer understand the true shape of the problem and inform the problem statement and future procurement strategy.
The approach
ACE set up the trials within its PodDev environment, a secure, segregated test space for evaluating different tools using real maritime sensor data (captured and replayed as live streams). This enabled repeated testing of scenarios using the same dataset, an independent comparison of different approaches, controlled access to sensitive information and rapid iteration without impacting live systems.
A market assessment of commercial off the shelf correlation engine providers was conducted across typically associated domains (maritime, air, road traffic) and identified suppliers were invited to join the Vivace community, if not already a supplier.
Four of ACE’s Vivace Community were selected through the standard CTF1 and CTF2 response, including both existing and new Vivace community members. Each participant was kept segregated from the others to protect future procurement integrity and commercial sensitivities.
What we learned
Through hands-on trials and deep technical engagement, the project revealed that correlation for JMSC would not be best achieved by a monolithic standalone component.
It needs to work as part of an evolving broader Maritime Domain Awareness (MDA) system and cannot simply be “slotted in”. Data quality and preprocessing are critical.
Suppliers demonstrated that improving data quality would make correlation significantly easier and more reliable. Different community members offered unique strengths. From machine learning-enhanced preprocessing to deep understanding of correlation and fusion techniques, each brought new insights to the table.
And finally, that operational fit matters.
The project necessarily had to include a lightweight systems engineering analysis to understand the existing JMSC architecture and where new functionality could realistically sit, informing a more modular, flexible future solution approach.
Outcomes
A fully informed SOR, shaped by hands-on trials and grounded in operational reality combined with a clear understanding that the future correlation capability will likely be a custom-built integration of multiple components, not a single solution.
The commission provided valuable insight into the capability gaps and integration challenges across current systems and the identification of key design considerations for any future procurement, including zoning, data quality and data richness.
Customer feedback
The JMSC customer praised the project for reshaping their understanding of what was needed. The Chief Technology Officer described the findings as a “revelation”, particularly the discovery that a modular build would better fit the existing system than a traditional procurement of a single product.
This work showed how ACE can de-risk public sector procurement by testing assumptions early, engaging the market effectively and delivering insight that changes the course of complex technology programmes.
It also demonstrates the power of ACE’s PodDev testbed in supporting technical trials with real data, securely and at pace.
