Protect EW sensor-to-effector chains, SIGINT processing pipelines, DRFM jammer links, and cognitive EW decision loops against quantum computing threats. From PQC-secured jammer-controller communications to AI-driven adaptive EW with quantum-safe integrity across the contested electromagnetic spectrum.
ML-KEM key exchange secures the jammer-controller link, ensuring that DRFM technique commands and deception waveform data remain protected against quantum-capable adversaries.
End-to-end PQC-encrypted SIGINT data chain from intercept receiver through processing to exploitation, preventing harvest-now-decrypt-later attacks on signals intelligence.
ML-driven adaptive electronic warfare with PQC-secured sensor-to-effector loops, enabling real-time threat response while maintaining quantum-safe chain of custody for all EW decisions.
Electronic warfare systems depend on encrypted links between sensors, processors, jammers, and command nodes. SIGINT collection pipelines transmit high-value intercepted data using RSA and ECC-based encryption that quantum computers will break, exposing classified signals intelligence.
Quantum computers will decrypt intercepted SIGINT data retroactively through harvest-now-decrypt-later attacks. EW link encryption protecting jammer-controller communications and technique assignment will be compromised. Emerging quantum sensors will detect EW emissions with unprecedented sensitivity, making unpredictable waveform generation critical for survivability.
| Parameter | Value |
|---|---|
| Solutions Available | 6 quantum-safe solutions |
| FPGA Solutions | 4 (DRFM, SIGINT, QRNG, AI-EW) |
| ASIC Solutions | 1 (SIGINT processor) |
| COTS Solutions | 1 (crypto-agile data link) |
| Firmware & Platforms | 1 (threat library distribution) |
| AI-Integrated | 1 (cognitive EW engine) |
| Standards | FIPS 203, 204, CNSA 2.0 |
| Migration Phase | 2025-2030 (hybrid first) |
| Solution | Type | Description |
|---|---|---|
| PQC-Secured DRFM | FPGA | DRFM with ML-KEM key exchange for secure jammer-controller link, protecting deception technique commands and waveform data |
| Quantum-Safe SIGINT Processor | FPGA, ASIC | PQC-encrypted SIGINT data chain from intercept receiver through processing to exploitation and dissemination |
| QS-ESM Threat Library Distribution | Software | PQC-signed threat library updates with ML-DSA ensuring integrity and authenticity of ESM parameter files |
| QRNG for EW Waveform Generation | FPGA | True quantum randomness for unpredictable jamming waveforms, technique sequencing, and emission timing |
| AI-Cognitive QS-EW Engine | FPGA, Software | ML-driven adaptive EW with PQC-secured sensor-to-effector loop for real-time threat response and technique selection |
| Crypto-Agile EW Data Link | COTS | Hardware module with hybrid PQC+classical encryption for EW command-and-control data links |
| Solution | Description |
|---|---|
| PQC-Secured DRFM | Synthesizable RTL core implementing ML-KEM-768 key exchange integrated with DRFM controller FPGA. Secures jammer technique commands and deception waveform data with quantum-safe encryption. Field-upgradeable for algorithm agility. |
| Quantum-Safe SIGINT Processor | FPGA IP core providing PQC-encrypted data pipeline for SIGINT collection, processing, and dissemination. Supports real-time encryption of wideband intercept data with minimal latency impact. |
| QRNG for EW Waveform Generation | True quantum random number generator core for unpredictable jamming waveform parameters, technique sequencing, frequency hopping patterns, and emission timing to defeat quantum-era signal analysis. |
| AI-Cognitive QS-EW Engine | Neural network inference engine for adaptive EW technique selection with PQC-secured model loading, sensor data ingestion, and effector command output. Integrates with DRFM and ESM subsystems. |
| Solution | Description |
|---|---|
| Quantum-Safe SIGINT Processor | Hard IP for integration into SIGINT processing ASICs. Provides lowest latency PQC encryption for high-throughput intercept data chains in production EW systems with tamper-resistant design. |
| Solution | Description |
|---|---|
| AI-Cognitive QS-EW Engine | Machine learning-driven cognitive electronic warfare engine that autonomously classifies threats, selects optimal countermeasure techniques, and adapts EW responses in real time. The full sensor-to-effector decision loop is PQC-secured with ML-KEM encrypted data flows and ML-DSA signed technique commands for quantum-safe chain of custody. |
| AI-Enhanced SIGINT Analysis | ML-augmented signals intelligence processing with automated emitter identification, pattern-of-life analysis, and anomaly detection. All analytical outputs and classified intelligence products are PQC-signed for integrity and authenticated dissemination. |
Choose the delivery model that matches your electronic warfare system's integration requirements.
Complete source cores for integration into your EW processing FPGA or ASIC. Includes testbench, verification suite, and integration guides for DRFM, SIGINT, and cognitive EW subsystems.
Pre-characterized for Xilinx UltraScale+, Intel Agilex, or specific ASIC nodes. Guaranteed timing closure and performance for latency-critical EW applications.
SOSA-aligned 3U/6U VPX modules with hybrid PQC+classical encryption for EW data links, crypto-agile C2 connectivity, and standalone deployment in EW suites.
Quantum-safe solutions that complement electronic warfare system security.
Contact us for quantum vulnerability assessments, solution evaluations, or custom integration for your EW platform.