EchoNet — Distributed Sensing Infrastructure

ECHOCORE AI INC.

A distributed passive sensing mesh that detects drones where radar coverage is degraded by terrain, clutter, or cost — through acoustic, thermal, and RF cross-validation.

Passive

Three sensing modalities. Zero RF emissions. No jamming surface.

Distributed

No single point of failure. Coverage survives node loss.

Cross-Validating

Acoustic + thermal + RF fusion catches what single sensors miss.

Complementary

We cue radar and EO systems. We don’t replace them.

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Operational Context

The Problem Landscape

Three structural limitations define the current CUAS environment. EchoNet addresses each through distributed sensing architecture.

Emerging Threats Are Bypassing Conventional Detection

RF-silent UAS, tethered platforms, and swarm operations were not the design target of current CUAS architectures. Detection diversity is now a structural requirement, not an option.

Centralized Systems Create Single Points of Failure

High-value sensors are high-value targets. Jamming, spoofing, or physical neutralization of one node degrades entire coverage zones.

Wide-Area Coverage Remains Cost-Prohibitive

Linear borders, forward bases, and urban perimeters demand persistent monitoring. Conventional deployments cannot scale to meet this geometry.

System Design

Sensing Architecture

Three passive modalities. One intelligent fusion layer.

Layer 01

Acoustic Layer

Primary Detection

50–100m range85%+ accuracyPassive

MEMS microphone array with CNN-based drone signature classification. The primary detection modality — passive, always-on, and effective in complex terrain where radar suffers from ground clutter.

50–100m detection range (FPV-class drones)

CNN classification, 85%+ benchmark accuracy (controlled conditions; field validation ongoing)

Operates independent of RF emissions

Effective in urban canyons and forested terrain

Layer 02

Thermal Layer

Close-Range Confirmation

20–50m range180° rotationAcoustically cued

Wide-FOV infrared sensor with 180° motorized rotation. Not a search sensor — a confirmation sensor. Cued by acoustic detections, it rotates toward the suspected sector to verify presence.

20–50m confirmation range

Motorized 180° field rotation, acoustically cued

Filters vehicles, birds, and static heat sources

Operates day and night, independent of lighting

Layer 03

RF Layer

High-Altitude Awareness

300–500m AGL400MHz–6GHzZero emissions

Wideband passive SDR receiver covering 400MHz–6GHz. Ensures EchoNet is never blind above the acoustic and thermal envelope — including drones flying at 300–500m AGL where acoustic detection degrades.

300–500m AGL awareness (emitter-dependent)

Passive receive-only, zero RF emissions

Band classification: 433MHz / 2.4GHz / 5.8GHz / sub-6GHz emitters

No interference with existing CUAS assets

Layer 04

AI Fusion Core

Cross-Validation Intelligence

3 inputs5 threat verdictsReal-time fusion

The fusion layer is what transforms three individually imperfect sensors into a reliable decision system. Threat type is determined not just by what triggers — but by what doesn't.

Acoustic + Thermal + RF → high-confidence RF-emitting UAS

Acoustic + Thermal, no RF → likely fibre-tethered or RF-silent platform

RF only → distant high-altitude emitter, beyond acoustic range

Acoustic only → probable false alarm, low priority

Threat Intelligence

What the Fusion Layer Sees

Threat type is determined by which modalities agree — and which don't.

ACOUSTIC

THERMAL

VERDICT

ACOUSTIC✓ Detected

THERMAL✓ Detected

RF✓ Detected

High-confidence RF-emitting UAS

ACOUSTIC✓ Detected

THERMAL✓ Detected

RF✗ Silent

Likely fibre-tethered or RF-silent platform

ACOUSTIC✗ Silent

THERMAL✗ Silent

RF✓ Detected

Distant high-altitude emitter — cue radar

ACOUSTIC✓ Detected

THERMAL✗ Silent

RF✗ Silent

Probable false alarm — low priority

ACOUSTIC✓ Detected

THERMAL✓ Detected

RF✗ Silent

RF-silent swarm — elevated threat

Fusion logic reduces false alarms and identifies threat class — including threats invisible to RF-only systems.

System Role

Where EchoNet Fits

EchoNet is not a radar replacement. It is the persistent passive layer that fills the gap radar cannot see — and then cues the systems that can respond.

What Radar Covers

High-speed fixed-wing threats (>200 km/h)
High-altitude fixed-wing aircraft
Long-range volume search (5–15 km)
Wide-area volume search

Where EchoNet Operates

Low, slow, small UAS — below radar floor
RF-silent and fibre-tethered platforms
Urban canyons and terrain-masked zones
Close-in perimeter and critical infrastructure

What EchoNet Cues

Radar slew-to-cue on threat bearing
EO/IR camera pointing and tracking
Operator alert with threat classification
C2 handoff with sensor confidence score

We are the tripwire. Radar is the response.

Interoperability

Integration & Interoperability

EchoNet is not a closed system. The architecture prioritizes integration readiness at every layer — from sensor output to command interface — ensuring compatibility with existing and future operational frameworks.

Architecture-ready for integration with external C2 systems. Not a standalone endpoint — a distributed sensing extension designed to feed into existing operational data flows.

Structured Data Output

Network outputs are structured for downstream consumption by command and control platforms. Data formats are designed with interoperability as a primary architectural constraint.

C2 Protocol Compatibility

Architecture designed for compatibility with digital C2 data flow standards including CoT and SAPIENT-type message structures. Integration pathways do not require modification of upstream systems.

API-Based Integration Layer

An API-based data layer enables modular connection to external processing, display, and decision-support systems without requiring proprietary middleware.

Non-Disruptive Integration

EchoNet is architected as a complementary sensing layer. Deployment augments existing sensor coverage without requiring replacement or reconfiguration of installed systems.

CUAS Sandbox 2026

Suffield Demonstration Objectives

The following objectives define the scope of EchoNet's validation activities at CFB Suffield. Each objective targets a discrete capability claim within current TRL parameters.

Event

CUAS Sandbox 2026

Host

DND / CAF

Location

CFB Suffield, Alberta

Timeline

September 2026

Suffield Demonstration Scope

Will Demonstrate

Multi-node acoustic detection, thermal confirmation cueing, RF emitter awareness, and AI fusion verdict output across a 5-node deployment.

Out of Scope

Precise geo-location, swarm differentiation, and CoT/SAPIENT live integration are not claimed for this demonstration phase.

Success Criteria

Acoustic detection of FPV-class UAS at 50m+

Thermal confirmation within 10 seconds of acoustic cue

RF emitter classification by frequency band

Fusion verdict output with threat type and confidence score

OBJ-01

Distributed Geometry Validation

Validate multi-node deployment configurations against defined coverage geometries, including linear boundary and perimeter scenarios.

OBJ-02

Multi-Node Synchronization

Demonstrate reliable coordination between distributed sensing nodes under field conditions, validating network-level consistency and data coherence.

OBJ-03

RF-Silent Detection Pathway Validation

Validate detection capability through non-RF sensor pathways, addressing threat profiles that bypass conventional RF-dependent detection architectures.

OBJ-04

Tethered UAS Threat Recognition

Assess network response to tethered platform threat profiles, leveraging sensor diversity and distributed geometry to identify disturbance signatures.

Technology Maturity

Maturity & Path Forward

EchoNet is at TRL 5–6 transition. The following reflects an accurate account of what has been validated, what is under active refinement, and what will be tested at Suffield.

Current TRL

5 → 6

TRL 1TRL 5–6TRL 9

Validated

Distributed node architecture design and simulation

Network topology modeling for linear and perimeter geometries

Multi-node data fusion logic and correlation algorithms

Passive detection pathway identification and feasibility assessment

C2 output data structure and format specification

Under Refinement

Hardware prototype integration and field calibration

Real-environment multi-node synchronization under variable conditions

Detection confidence threshold refinement across sensor modalities

To Be Tested at Suffield

End-to-end system performance under operational field conditions

Coverage geometry validation against defined boundary scenarios

Multi-node coordination under Red Team UAS threat simulation

Development Roadmap

2026 Q3

CUAS Sandbox

Field validation at CFB Suffield. Architecture-level demonstration under operational conditions.

2026 Q4

Post-Sandbox Refinement

Integration of field feedback. Sensor fusion algorithm refinement and hardware iteration.

2027

TRL 6 → 7 Transition

Expanded real-world trials. Mobile platform integration and production architecture design.

2028

Production Readiness

Scalable deployment architecture. Broadened operational environment compatibility.