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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.

Scientific data

|February 10, 2026

Resumen

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.

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.

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