The Challenge
Modern signal intelligence operations confront a spectrum environment of unprecedented complexity. RF emitters proliferate across contested battlespaces, commercial infrastructure, and denied areas — generating sensor data volumes that outpace manual exploitation by orders of magnitude. Most organizations face four compounding problems:
- Fragmented RF spectrum coverage. Sensors operate in isolation: each collection asset produces raw IQ samples or basic demodulated output, with no common schema for downstream processing.
- Multi-source fusion gaps. ELINT, COMINT, and ESM feeds are stored in separate databases with incompatible formats, preventing the cross-domain correlation that produces high-confidence intelligence.
- Real-time correlation latency. Emitter parametric data must be matched against target libraries, historical tracks, and threat models within seconds — not the hours that batch-processing pipelines require.
- Missing geospatial context. Signals bearing and geolocation data rarely flows automatically into GEOINT overlays, forcing analysts to perform manual cross-referencing between SIGINT and mapping tools.
Corvus Intelligence addresses all four dimensions through purpose-built SIGINT platform development: sensor-agnostic ingestion, real-time fusion, ML-driven classification, and native geospatial integration.
What We Build
Our engineering teams design and deliver production-grade signal intelligence software across the full collection-to-exploitation chain.
RF Collection & Processing Pipelines
High-throughput ingestion layers that normalize raw I/Q and demodulated output from heterogeneous sensor arrays into a unified stream for downstream analysis and storage.
ELINT / COMINT / ESM Data Fusion
Cross-domain fusion engines that correlate electronic intelligence, communications intelligence, and electronic support measures records into unified emitter tracks with confidence scores.
Target Classification with ML Models
PyTorch and TensorFlow classification pipelines trained on labeled emitter parametrics, modulation fingerprints, and behavioral patterns — enabling automated threat library matching at scale.
Geospatial Correlation (GEOINT Overlay)
Native PostGIS and GDAL integration pipelines that project signal bearing, time-difference-of-arrival, and multilateration results onto map layers with dynamic uncertainty ellipses and track history.
Offline / Disconnected Operation
Air-gapped deployment architectures with local inference, on-device threat libraries, and store-and-forward synchronization — enabling full-capability SIGINT exploitation without network connectivity.
Cross-Domain Data Exchange
Compliant adapters for STANAG 4609 motion imagery metadata and STANAG 7085 interoperable data links, enabling intelligence product distribution across coalition and NATO systems.
Built With Corvus.Wings
Every SIGINT platform we build benefits from operational lessons learned developing our own fielded signal intelligence product.
Geo-Referenced WiFi Device Tracking
Corvus.Wings is our operational signal intelligence platform that performs passive WiFi probe collection, device fingerprinting, and geo-referenced track association at scale — without forward-deployed hardware. It demonstrates the RF collection and geospatial correlation capabilities we bring to every custom SIGINT platform engagement. The patterns we developed for Corvus.Wings — sensor normalization, real-time track fusion, and Mapbox GL geospatial rendering — directly inform our custom development methodology.
Our Approach
SIGINT platform development is not generic software delivery. We apply a three-phase methodology shaped by operational experience with real RF environments and intelligence workflows.