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SDR Drone Detection System

AI-Driven SDR-Based Drone Detection & Mitigation Platform This project presents a production-grade, end-to-end drone detection and mitigation system built on Software Defined Radio (SDR) and advanced machine learning. It is designed for real-world deployment, not just experimentation, combining signal intelligence, AI inference, scalable deployment, and hardware abstraction into a single cohesive architecture. Unlike simple spectrum scanners or single-purpose scripts, this platform delivers a complete operational ecosystem—from raw RF signal acquisition to intelligent classification, localization, tracking, and response—while remaining modular, extensible, and hardware-agnostic Why This System Stands Out • True End-to-End Architecture Covers the full lifecycle: RF capture → signal processing → ML detection → classification → localization → tracking → mitigation → visualization. • Multi-Hardware SDR Support Designed to work seamlessly with HackRF, BladeRF, USRP, RTL-SDR, simulated devices, and future SDR platforms through a clean driver abstraction layer. • AI-First Design Integrates classical RF detection methods with modern ML models (ONNX-based), including signal detection, drone classification, anomaly detection, and behavioral analysis. • Deployment-Ready Supports Docker, Kubernetes, edge devices, GPU acceleration, FPGA offloading, and cloud-scale deployments—making it suitable for fixed sites, mobile units, and distributed sensor networks. • Real-World Focus Includes calibration, performance benchmarking, hardware testing, CI/CD pipelines, monitoring, logging, security scanning, and rollback mechanisms—features often missing from academic or hobby projects. Who This Is For This platform is ideal for: • RF and SDR professionals • AI/ML engineers working with signal intelligence • Researchers in counter-UAS and spectrum surveillance • Organizations building operational drone detection systems, not just prototypes What You Get • A clean, scalable, and future-proof architecture • Production-ready ML pipelines and models • Hardware abstraction that reduces vendor lock-in • Comprehensive documentation and testing structure • A strong foundation for commercial, research, or governmental deployment