{"id":48083761,"url":"https://github.com/ronibandini/drone-detection","last_synced_at":"2026-04-04T15:03:20.349Z","repository":{"id":346655964,"uuid":"1190997884","full_name":"ronibandini/drone-detection","owner":"ronibandini","description":"Maker devices for drone detection and identification","archived":false,"fork":false,"pushed_at":"2026-03-25T12:44:41.000Z","size":6445,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-03-26T01:51:21.867Z","etag":null,"topics":["dji","drone","edge-impulse","machine-learning","remote-id"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ronibandini.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2026-03-24T20:28:36.000Z","updated_at":"2026-03-25T12:44:45.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/ronibandini/drone-detection","commit_stats":null,"previous_names":["ronibandini/drone-detection"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/ronibandini/drone-detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronibandini%2Fdrone-detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronibandini%2Fdrone-detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronibandini%2Fdrone-detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronibandini%2Fdrone-detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ronibandini","download_url":"https://codeload.github.com/ronibandini/drone-detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronibandini%2Fdrone-detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31403952,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-04T10:20:44.708Z","status":"ssl_error","status_checked_at":"2026-04-04T10:20:06.846Z","response_time":60,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dji","drone","edge-impulse","machine-learning","remote-id"],"created_at":"2026-04-04T15:03:15.463Z","updated_at":"2026-04-04T15:03:20.340Z","avatar_url":"https://github.com/ronibandini.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Dispositivos maker para detectar drones y reaccionar\n\n\u003cimg width=\"800\" height=\"1127\" alt=\"DispositivosMakerParaDetectarDrones\" src=\"https://github.com/user-attachments/assets/9759b5fb-a384-4d50-8d2e-3c9245c2cfe4\" /\u003e\n\n**Estos proyectos fueron preparados para el  PRIMER SIMPOSIO INTERNACIONAL DE CIBERSEGURIDAD ECC-UCM COLOMBIA** **Autor:** Roni Bandini\n\n---\n\n## 🛠️ Pruebas de concepto\n\n### 1. Captura de REMOTE ID vía ESP32\nDetección de señales de identificación remota utilizando el estándar actual de la industria.\n\n* **Hardware:** * Beetle ESP32C6\n    * Switch conectado entre **D4** y **GND**\n    * Gabinete en dos piezas de PLA (ensamblado con 2 tornillos M3)\n* **Instalación:** * Conectar la placa ESP32 con cable USB.\n    * Cargar el software `droneRemoteID.ino.merged.bin` con **ESPTools** en la dirección `0x000`.\n* **Modos de Operación:**\n    * **Switch ON:** Modo *Sniff* (rastreo de señales).\n    * **Switch OFF:** Modo *View* (interfaz de usuario).\n* **Acceso a Datos:**\n    1. Conectarse al WiFi `Drone-Detector`.\n    2. Cargar en el navegador la IP `192.168.4.1`.\n    3. Visualizar registros de Remote ID para piloto/drone, links de geolocalización y botón de purga de registros.\n    *Nota: Es necesario desconectarse del AP para acceder a los links de mapas externos.*\n\n---\n\n### 2. Detección de drones vía audio con Machine Learning\nIdentificación acústica mediante modelos de inferencia entrenados para reconocer motores brushless.\n\n* **Hardware:** Raspberry Pi 5 y micrófono USB.\n* **Preparación del sistema:**\n    ```bash\n    sudo apt install python3-gpiozero\n    ```\n* **Configuración de Edge Impulse:**\n    1. Registrarse en [Edge Impulse](https://studio.edgeimpulse.com/studio/937618) y clonar el proyecto.\n    2. Instalar el entorno en la Raspberry Pi 5 siguiendo la [documentación oficial](https://docs.edgeimpulse.com/hardware/boards/raspberry-pi-5).\n* **Ejecución:**\n    1. Ejecutar el runner para verificar inferencias en pantalla:\n       ```bash\n       edge-impulse-linux-runner\n       ```\n    2. Interrumpir con `CTRL+C` una vez validado.\n    3. Iniciar el script de detección final:\n       ```bash\n       python3 dronedetection2.py\n       ```\n\n---\n\n### 3. Detección vía análisis del espectro\nMonitoreo de señales no cooperativas mediante el escaneo de portadoras en la banda de 2.4 GHz.\n\n* **Hardware:** ESP32 + nRF24L01+PA+LNA.\n* **Lógica de detección:** Identificación de patrones de **FHSS** (Frequency Hopping Spread Spectrum) para diferenciar drones de ruidos de fondo o redes WiFi estáticas mediante el análisis de persistencia y dispersión de energía.\n\n---\n\n## 📩 Contacto\n\n**Roni Bandini** [LinkedIn Profile](https://www.linkedin.com/in/ronibandini/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fronibandini%2Fdrone-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fronibandini%2Fdrone-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fronibandini%2Fdrone-detection/lists"}