Defense R&D AI

RF Signals Intelligence

ML-driven signal identification, AoA/TDoA localization, and automated alerting for contested spectrum environments. Enables operators to track and identify emitters, building pattern-of-life intelligence through persistent monitoring.

01 / Problem

Contested spectrum requires automated signal intelligence

Defense organizations operating in contested electromagnetic environments needed the ability to detect, identify, and locate RF emitters in real-time. Manual spectrum analysis could not keep pace with the density and complexity of modern signal environments, where thousands of emitters operate simultaneously across wide frequency ranges.

The core challenge was building a system that could automatically classify signals of interest from background noise, geolocate emitters using distributed sensor networks, and alert operators to tactically relevant activity — all while operating in degraded, contested, or denied environments.

Without automated SIGINT capabilities, operators lacked situational awareness of the electromagnetic battlespace, limiting their ability to respond to threats and exploit intelligence opportunities.

Key Constraint

The system needed to operate with minimal latency for real-time alerting while processing high-bandwidth RF data streams from multiple geographically distributed sensors.

02 / Process

ML pipeline from raw RF to actionable intelligence

The engagement delivered a complete SIGINT processing pipeline from RF front-end through ML classification to operator display, with geolocation and pattern-of-life analysis capabilities.

Assess

Characterized target signal environments and defined signals of interest. Evaluated SDR hardware options and sensor deployment geometries. Established latency and accuracy requirements for real-time operations.

Design

Architected ML pipeline for signal detection, classification, and feature extraction. Designed AoA/TDoA geolocation algorithms for distributed sensor fusion. Specified operator interface for real-time alerting and pattern-of-life visualization.

Build & Deploy

Implemented signal processing pipeline with GPU-accelerated ML inference. Deployed distributed sensor network with time-synchronized data collection. Built operator workstation with geospatial display and automated alerting.

Advise & Improve

Trained operators on system capabilities and tactical employment. Continuously refined ML models with new signal types encountered in the field. Extended pattern-of-life analysis for long-term intelligence development.

Machine Learning SDR AoA/TDoA Signal Processing GPU Inference GIS

03 / Product

Automated spectrum awareness for contested environments

The engagement delivered an operational SIGINT system that automatically detects, classifies, and geolocates RF emitters of interest. Operators receive real-time alerts for tactically relevant signals, with emitter tracks displayed on a geospatial interface.

The system's pattern-of-life analysis capabilities enable long-term intelligence development, correlating emitter activity over time to build understanding of adversary communications patterns and operating procedures.

Detection

ML-driven signal classification

Localization

AoA/TDoA geolocation

Alerting

Real-time operator notification

Analysis

Pattern-of-life intelligence

Impact

The system provides persistent electromagnetic situational awareness that was previously impossible with manual analysis, enabling proactive response to threats and exploitation of intelligence opportunities.

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