MESH-D

MESH-D

Distributed Maritime Acoustic Intelligence

A rapidly deployable network of autonomous passive-acoustic sensing nodes for maritime awareness, critical infrastructure monitoring and ocean science.

Explore the conceptDiscuss a pilot

Concept development • Simulation • Prototype programme

  • Passive sensing
  • Edge processing
  • Distributed networking
  • Environment-aware deployment

01The idea

See more of the maritime environment.

Much of maritime awareness depends on what can be seen, transmitted or imaged above the surface.

Mesh-D adds another layer.

A distributed network of autonomous floating nodes listens below the surface, processes acoustic activity locally and shares relevant observations with wider maritime systems.

Surface

  • AIS
  • Radar
  • Satellite
  • EO
+

Below surface

  • Passive acoustic sensing
=

Multi-modal maritime awareness

  • Independent layers, correlated observations

Mesh-D is designed as a complementary sensing layer — not a replacement for AIS, radar, satellite or existing sonar systems.

02The system

One system. Three products.

Mesh-D is a sensing puck, the runtime that operates it at sea, and the Mesh-D Maritime Intelligence Platform that plans and runs the network from shore. Each is built for the other two.

Mesh-D Mission & Control

Cloud / HQ

Mission

  • INFOMAR / ocean acoustic model
  • Deployment optimiser
  • Drift + energy forecast

Control

  • Live fleet + alerts
  • Device management
  • Commands

Fusion

  • Multi-node correlation
  • AIS association
  • Contact hypotheses
Secure API / MQTT
  • Satellite
  • LTE
  • LoRa

Mesh-D Node

At sea

Mesh-D OS

Mesh-D Core

Zephyr RTOS · STM32

  • Power + acquisition
  • Watchdog + health
  • Low-power detector

Mesh-D Edge

Linux · ARM SoM

  • DSP / AI
  • Storage
  • Classification

Hydrophone

Concept architecture. Components and interfaces subject to engineering.

03Mesh-D Node

Designed for deployment, not the laboratory.

Every subsystem is chosen for unattended operation at sea and straightforward replacement ashore.

Passive hydrophone

Configurable sensor depth below surface noise.

Edge compute

Low-power always-on detection with event-triggered higher-performance processing.

GNSS + timing

Position, synchronisation and deployment tracking.

Multi-path comms

Satellite, cellular and LoRa-class local communications.

Solar + battery

Energy-aware architecture for extended unattended operation.

Drogue stabilisation

Independent load path to reduce wind-driven drift.

Local storage

Retain high-resolution acoustic data while transmitting compact events.

AIS receive

Correlate cooperative vessel traffic with acoustic observations.

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Concept configuration

Nominal diameter
560 mm
Typical hydrophone deployment range
15–30 m
Indicative battery class
~500 Wh
Indicative solar class
20–30 W
Communications options
Sat / LTE / LoRa

Illustrative concept configuration. Final specifications subject to engineering and sea-trial validation.

04Mesh-D OS

Always listening. Awake only when it matters.

Mesh-D OS is the embedded runtime inside every node. It spans two processors: a microcontroller that never stops listening, and a Linux edge processor that powers up only when there is something worth analysing.

Mesh-D Core

Always on

Runtime
Zephyr RTOS
Silicon
STM32 microcontroller
  • Power management
  • Acoustic acquisition
  • Watchdog + health
  • Low-power detector

Mesh-D Edge

Wakes on event

Runtime
Linux
Silicon
ARM system-on-module
  • DSP + AI inference
  • High-resolution storage
  • Classification
  • Compact event reporting

One detection cycle

  1. 01

    Core listens continuously at microcontroller power.

  2. 02

    The low-power detector trips and Core wakes Edge.

  3. 03

    Edge classifies the event and stores the full-resolution audio.

  4. 04

    A compact event goes to Mesh-D Control; Edge powers down.

05The network

One node listens.
A network creates context.

Individual passive sensors can identify acoustic activity.

Distributed nodes allow observations to be compared across space and time — increasing confidence, reducing ambiguity and enabling network-level detection and localisation research.

A Mesh-D node on station: surface float at the waterline, tether descending to a hydrophone at 30 m and a drogue at 7 m
Concept render of a single node on station. A field of these drifts independently; each contributes one listening point to the network.

  1. Node

    Detection

    then

  2. Multiple nodes

    Corroboration

    then

  3. Network

    Contact confidence + spatial context

Unassociated acoustic contact

Nodes
D-07 D-11 D-12
AIS correlation
None
Classification
Vessel-like acoustic source
Confidence
High

Illustrative software concept — not live data.

06Mesh-D Mission & Control

The sensor is only half the system.

Underwater acoustic performance changes with location, depth and environmental conditions. Mission models those conditions before a single node enters the water; Control runs the network once it is deployed.

Plan

  • INFOMAR / bathymetry
  • Oceanographic data
  • Sound speed
  • Seabed
  • Sea state
  • Target profile
  1. 01Acoustic propagation model
  2. 02Deployment optimiser
  3. 03Mesh-D plan
  4. 04Live network

Mission is intended to determine:

  • Candidate node locations
  • Hydrophone depth
  • Node spacing
  • Predicted detection probability
  • Multi-node overlap
  • Drift
  • Energy endurance
  • Communications availability
  • Deployment persistence

One sensor = one fixed detection circle.

Environment-specific predicted performance.

SurfaceSeabedHydrophoneAcoustic source
Conceptual only: propagation depends on sound-speed profile, bathymetry and sea state — not on a fixed radius.

Operate

  • Fleet + detections

    Every node's position, drift, health and energy state alongside live acoustic events.

  • Fusion

    Correlate events across nodes into contact hypotheses, with AIS as a separate line of evidence.

  • Device management

    Configuration, diagnostics and Mesh-D OS updates across the deployed fleet.

  • One environment model

    Observations from the live network feed back into the acoustic model Mission plans against.

07Maritime domain awareness

An additional sensing layer for MDA.

Mesh-D is intended to provide detection, corroboration and cueing information to wider maritime awareness systems. Passive acoustics alone does not confirm identity — it strengthens the picture.

Non-cooperative activity

Provide acoustic evidence of activity that may not correspond to AIS or other cooperative signals.

Critical infrastructure

Temporary or persistent monitoring around subsea cables, offshore infrastructure and strategic maritime areas.

Sensor fusion

Provide independent observations to systems combining AIS, radar, satellite, EO/IR and other intelligence sources.

Rapid deployment

Deploy temporary sensing fields without installing large permanent infrastructure.

Cueing

Use distributed passive observations to direct higher-performance or higher-cost sensing assets.

08Science and civil applications

The same network. Different mission.

The scientific and security applications reinforce each other. Better environmental models improve acoustic predictions; better acoustic datasets improve detection and classification models.

Passive acoustic monitoring

Continuous or event-triggered underwater acoustic observation.

Marine mammal research

Support research into presence, distribution and behaviour.

Underwater noise

Characterise vessel, infrastructure and ambient sound.

Offshore development

Environmental baseline and operational monitoring around offshore energy and infrastructure.

Oceanographic research

Combine acoustic data with environmental observations and drifting-platform measurements.

Mesh-D node deployed for environmental monitoring

09Why distributed

Designed around quantity, flexibility and loss tolerance.

Traditional maritime sensing can depend on small numbers of expensive assets. Mesh-D explores a different trade: lower-cost nodes, more sensors, distributed risk, simpler deployment, configurable mission duration, replaceable COTS subsystems and software-defined deployment planning.

Traditional model

  • Few
  • High-value
  • Complex
  • Persistent infrastructure

Mesh-D model

  • Distributed
  • Deployable
  • Modular
  • Scalable by mission

The aim is not to outperform every high-end sensor. The aim is to provide useful additional information at a deployment cost and scale that enables sensing where conventional systems may be impractical.

10Deployment profiles

One platform. Multiple deployment profiles.

Mesh-D

  • Rapid deployment
  • Short-to-medium missions
  • Oceanographic sensing
  • Temporary MDA fields
  • Current-following observation

11Deployment sequence

From model to mission.

  1. 01

    Model the environment

    Bathymetry, sound speed, currents and mission constraints.

  2. 02

    Plan the network

    Determine node positions, depths and expected coverage.

  3. 03

    Deploy

    Rapidly deploy sensors from suitable vessels.

  4. 04

    Sense + process

    Detect locally and share compact observations.

  5. 05

    Fuse + respond

    Correlate Mesh-D observations with wider maritime data.

12Research foundation

Built on established science. Focused on a new system architecture.

Passive acoustics

Decades of underwater acoustic science and passive monitoring.

Autonomous marine sensors

Small, low-power oceanographic platforms already operate for extended periods at sea.

Edge AI

Low-power event detection increasingly allows complex processing to move onto the sensor.

Environment-aware sensor planning

Modern acoustic models, bathymetry and optimisation methods allow deployment to be designed around real environmental conditions.

View technical basis

13Development status

From concept to sea trial.

Early stage, technically disciplined: each phase gates the next.

Now

current
  • Mission definition
  • Hardware architecture
  • Environmental simulation
  • Acoustic modelling

Next

  • Digital-twin validation
  • COTS engineering prototypes
  • Controlled vessel trials

Then

  • Multi-node sea trial
  • Model calibration
  • Operational pilot

Interested in testing where distributed passive sensing can add value?

We are exploring research, maritime-security, offshore and scientific pilot applications for Mesh-D.

Discuss a pilotTechnical collaboration