End-to-End System Architecture
Real-time Electronic Warfare support combining Deep Reinforcement Learning with Graph Attention Networks (GAT).
1. RF Front-End & DSP
Captures 2.0–18.0 GHz spectrum, computes high-speed FFT channel energy, and generates PSD matrices.
2. GNN Graph Attention
Models frequency channels as nodes and correlation transition probabilities as dynamic graph edges.
3. DRL Predictive Tuner
Deep Q-Network decides optimal receiver tuning 1 hop in advance, achieving sub-2ms reaction latency.
Dual-Core AI Decision Loop
How the Scanner (GNN) and Player (DQN) cooperate to outsmart enemy frequency hoppers.
Part 1: The Scanner (GNN Spatial Extractor)
• Ingests continuous spectrum energy across all monitored bands.
• Extracts hidden spatial correlations between hopping channels.
• Produces relational state embeddings for the decision agent.
Part 2: The Player (DQN Policy Agent)
• Evaluates current state embedding against trained Q-value network.
• Predicts enemy emitter's next hop transition with >94% accuracy.
• Instantly tunes the receiver front-end before the emitter fires.
Performance Benchmarks vs Traditional EW Systems
Tested against agile Frequency Hopping Spread Spectrum (FHSS) radar emitters.
Detection Probability (Pd)
Traditional: 35% | Smart Scan EW: 95.2% (+172%)
35% (Sweeper)
95.2% (Smart Scan EW)
False Alarm Rate (Pfa)
Traditional: 15.0% | Smart Scan EW: 1.1% (13x Reduction)
15% (High Noise)
1.1% (Filtered)
Interception Latency
Traditional: 45 ms | Smart Scan EW: 1.8 ms (25x Faster)
45 ms (Blind Sweeping)
1.8 ms (Predictive)