Learning an Opponent-aware Anti-jamming Strategy via Online Convex Optimization

This project is a reference implementation for a specific academic paper in the Electronic Warfare (EW) niche. With 0 stars and 4 forks only 4 days after release, it represents a very early-stage research artifact. Defensibility is low because the code serves as a proof-of-concept rather than a production-ready tool; the value lies in the mathematical approach (OCO) rather than a software moat. Frontier labs like OpenAI or Google are unlikely to enter the radar signal processing space directly, making frontier risk low. However, the market for such technology is highly consolidated among major defense contractors (Raytheon, Thales, Northrop Grumman) who would likely reimplement these algorithms in proprietary C++/HDL stacks rather than using this specific repo. The use of OCO over traditional Reinforcement Learning is a strategic choice for sample efficiency in high-stakes, low-latency radar environments, but the displacement horizon is short as newer adaptive signal processing techniques (like Transformer-based waveform design) are rapidly emerging in research.