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Un conjunto de datos de drones multisensores sincronizados en el tiempo adquiridos de múltiples radares y receptores

Seung-Kyu Han1, Young-Ho Jung2

  • 1School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si, 10540, Republic of Korea.

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Un nuevo conjunto de datos de drones multi-sensores combina el radar de onda continua modulada por frecuencia (FMCW), el radar de onda continua (CW) y las señales de radiofrecuencia (RF). Este recurso ayuda en el desarrollo de sistemas avanzados de detección y clasificación de drones.

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Área de la Ciencia:

  • Robótica y sistemas de control de los sistemas de control.
  • Procesamiento de señales Procesamiento de señales.
  • La inteligencia artificial es inteligencia artificial.

Sus antecedentes:

  • La proliferación de aviones no tripulados presenta desafíos de seguridad significativos.
  • Los conjuntos de datos existentes a menudo carecen de datos de sensores multimodales, lo que dificulta el desarrollo integral del sistema.
  • Hay una necesidad de datos multi-sensores sincronizados para una detección robusta de drones.

Objetivo del estudio:

  • Introducir un conjunto de datos multi-sensor novedoso, sincronizado en el tiempo, para la detección y clasificación de drones.
  • Facilitar el desarrollo y la evaluación de soluciones de seguridad de drones basadas en IA.
  • Para permitir la investigación en la fusión de sensores multimodales y la clasificación consciente de la distancia.

Principales métodos:

  • Recogió señales sincronizadas en el tiempo de los radares FMCW, CW y receptores de RF.
  • Se obtuvieron datos de cuatro aviones no tripulados comerciales y un objetivo no tripulado a diferentes distancias (2-30m).
  • Se incluyen señales brutas y procesadas (mapas de alcance Doppler, espectro Doppler, densidades espectrales de potencia).

Principales resultados:

  • Un conjunto de datos completo que permite la comparación directa y la fusión de señales de múltiples sensores.
  • Facilitó el análisis a nivel de señal y basado en imágenes para la detección de drones.
  • Proporcionó un recurso confiable para evaluar algoritmos de detección basados en IA.

Conclusiones:

  • El conjunto de datos presentado es crucial para el avance de las tecnologías de detección y clasificación de drones.
  • Apoya el desarrollo de estrategias multimodales de fusión de sensores y modelos de aprendizaje de IA.
  • Este recurso acelerará la investigación en seguridad y vigilancia de drones.