Breaking the Electronic Warfare Silo. An independent Bayesian intelligence layer that reasons about what is probably happening rather than simply whether something crossed a threshold line.
Real-time Bayesian RF Manifold Scan
Every critical system depends on RF signals: navigation, aviation, maritime, communications, border surveillance, force protection. Attacks at the physics layer are cybersecurity problems — but existing tools share one critical architectural constraint.
Leading players — CRFS, Rohde & Schwarz, L3Harris, Northrop Grumman, Raytheon, Dedrone — are serious, capable systems. But they all share one architectural constraint: hardware and software are designed, sold, and deployed together as a single integrated unit.
This forecloses innovation at individual layers. You cannot drop in a different intelligence engine. You cannot simulate adversary behaviour before hardware is deployed. You cannot test protocols that postdate the signature database.
An independent intelligence layer — simulation-capable, mathematically rigorous, deployable on top of any RF capture infrastructure you already have.
Denies service by overwhelming a frequency band. Threshold tools trigger — but coordinated suppression can stay below threshold.
Replaces a legitimate signal with a false one. Indistinguishable to signature databases when using novel protocols.
Degrades entire operational pictures simultaneously — invisible to per-band threshold detection by architecture.
Targets control links of unmanned systems using protocols that postdate signature databases.
Physics-constrained Bayesian inference that no firmware update can defeat — because you cannot change the physics of what an adversary operation requires.
Standard RF monitoring treats each frequency band as an independent channel. MW treats the 25-band RF environment as a single geometric object — a curved manifold in 25-dimensional space — enabling coordinated attack detection that per-band tools miss entirely.
Priors defined from ITU-R propagation standards, Rician fading models, Johnson-Nyquist thermal noise, and causal coupling relationships. An adversary can change firmware, frequency allocation, waveform. They cannot change the physics of what their operation requires. Detection has no knowledge latency.
Off-manifold distance computed as a geodesic — the shortest path along the curved manifold surface — not Euclidean distance. Discriminates threat classes geometrically separated on the manifold but close in Euclidean terms. Output: calibrated Bayesian posterior over all possible threat explanations, not a binary alarm.
Every integrated EW system operates on real signals in real environments. But before deployment, before procurement, before doctrine is written — planners need simulation.
Signature databases have knowledge latency. MW physics priors do not — they encode what the physics must look like regardless of protocol.
Coordinated multi-band operations require joint geometric analysis across all bands simultaneously — impossible with per-band architectures.
The MW simulation framework identifies which gaps are performance issues (tunable) versus architectural limitations (structural). Critical for procurement decisions.
FPV swarm incursion with protocol-agnostic detection
GNSS spoofing via suppression-as-signal
Wideband SEAD sweep classification & geolocation
MALE drone ISR via satellite band correlation
IED trigger burst discrimination
Satellite uplink injection detection
Each scenario: physically realistic RF signatures + MW Bayesian inference + geolocation output + structured engagement recommendation. No current integrated EW product offers this.
The integrated model dominates operational deployment. MW offers the innovation layer that integrated products cannot provide.
| Capability | Integrated EW (CRFS, R&S, L3Harris) | MW Framework |
|---|---|---|
| Operational deployment on known threats | ✓ | ✓ |
| Pre-deployment simulation mode | ✗ | ✓ |
| Physics-prior based (no signature DB) | ✗ | ✓ |
| Multi-band joint geometric inference | ✗ | ✓ |
| Novel/post-database protocol detection | ✗ | ✓ |
| Capability gap analysis framework | ✗ | ✓ |
| Works on existing RF capture infra | ✗ | ✓ |
| Calibrated posterior (not binary alarm) | ✗ | ✓ |
For organisations working in defence, security, and critical infrastructure — counter-drone, EW doctrine development, RF security research.
For planners, decision-makers, and technical teams. Understand the MW approach, see the simulation framework in operation across relevant scenarios, identify capability gaps. No prior background in Bayesian statistics or EW required.
Deep dive into mathematical framework, prior definitions, simulation methodology, and the 15-scenario library. For technical teams integrating the intelligence layer with existing sensing infrastructure.
Develop bespoke scenarios for your specific operational environment. Integrate the intelligence layer with your sensing infrastructure for capability gap analysis tailored to your doctrine.
If you are working in counter-drone, critical infrastructure protection, EW doctrine development, or RF security research — let us show you how a Bayesian intelligence layer changes the capability gap analysis conversation.