Operational Environmental Intelligence for ASW Planning (Gemini)
Deploy autonomous sensor networks to characterize the acoustic battlespace, enabling accurate sonar predictions and tactical advantage in contested waters.
Key Benefits
Maximize Sonar Detection Probabilities
Optimize active and passive sonar settings based on actual acoustic propagation paths rather than historical averages, improving Figure of Merit calculations.
Ensure Continuity in Denied Environments
Maintain accurate environmental awareness at the tactical edge through autonomous processing, ensuring mission capability even when connectivity to shore is severed.
Exploit Acoustic Advantage
Identify and utilize shadow zones, surface ducts, and convergence zones to maneuver assets into positions of tactical superiority relative to adversary submarines.
Understanding the Problem
Mitigating Acoustic Uncertainty in Contested Waters
Successful Anti-Submarine Warfare (ASW) and mine countermeasures depend entirely on a precise understanding of the underwater environment. Sound is the primary sensor modality, yet its propagation is dictated by complex variables—specifically temperature, salinity, and pressure profiles—that change rapidly across time and distance. Commanders relying on historical climatology or sparse satellite data face significant risks; a slight miscalculation in the depth of the thermocline can render a Variable Depth Sonar ineffective or leave high-value units exposed in acoustic shadow zones. As modern diesel-electric submarines become quieter, the margin for error in acoustic prediction has effectively vanished.
This environmental uncertainty is compounded during operations in Denied, Disrupted, Intermittent, and Limited (DDIL) environments. Traditionally, fleet assets rely on shore-based Naval Ocean Processing Facilities (NOPFs) to crunch environmental models and transmit tactical decision aids back to the ship. In a contested spectrum where high-bandwidth backhaul is jammed or risky to use, the tactical edge is cut off from these centralized intelligence resources. Without local, self-sufficient sensing and processing capabilities, on-scene commanders are forced to fight blind, unable to update their Common Operational Picture with the reality of the water column beneath them.
How We Address This
Edge-Native Battlespace Characterization
To bridge the gap between historical models and tactical reality, defense forces are deploying distributed networks of autonomous sensing platforms capable of edge processing. By shifting the burden of environmental characterization from shore-based supercomputers to the point of collection, naval forces ensure persistent acoustic intelligence regardless of external connectivity.
Data Collection & Monitoring
A layered network of buoyancy-driven gliders and profiling floats continuously traverses the water column, using CTD sensors to map temperature and salinity from the surface to depth. Simultaneously, surface wave gliders and drifting buoys monitor wind speed and sea state. This data is processed on-board at the tactical edge to calculate real-time Sound Velocity Profiles (SVP) and quantify ambient noise levels.
Actionable Insights
The system autonomously converts raw oceanographic data into acoustic propagation models. It identifies critical features such as the sonic layer depth, the strength of the deep sound channel, and the precise location of convergence zones. Algorithms detect anomalies where in-situ conditions deviate from the standard climatology, triggering alerts for the watch floor to adjust sensor depth settings.
Impact
Commanders can confidently optimize the deployment of towed arrays and Variable Depth Sonars (VDS) to penetrate specific thermal layers. Tactical planners use this intelligence to route high-value units through areas where acoustic conditions favor counter-detection, while ASW assets can be pre-positioned in convergence zones to maximize detection range against dark vessels.
Recommended Systems (2)
This use case employs a multi-domain architecture to capture the complete acoustic picture. System 1 focuses on the water column structure to determine sound speed and refraction. System 2 focuses on the air-sea interface to quantify ambient noise, which is critical for determining the noise-limited detection threshold.
System configuration image
System Overview
Purpose
To map the physical properties of the water column for acoustic propagation modeling.
Deployment Context
Autonomous gliders or UUVs deployed forward of the battle group or in choke points for persistent monitoring.
Sensors
Required
Temperature
Essential for calculating sound speed; determines the location of the thermocline and mixed layer depth.
Salinity
Critical co-variable for sound speed calculation, particularly in littoral zones with freshwater runoff.
Pressure (Depth)
Required to correlate temperature and salinity data with specific depths to build the Sound Velocity Profile.
Important
Hydrophone
Measures ambient noise at depth and can provide opportunistic acoustic intercept of vessel signatures.