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Formación rápida de haces desconectados para matrices arbitrarias basadas en el aprendizaje bayesiano disperso fuera

Jianli Huang1, Yu Wang1, Zaixiao Gong1

  • 1State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, Chinahuangjianli@mail.ioa.ac.cn, wy@mail.ioa.ac.cn, gzx@mail.ioa.ac.cn, nhq@mail.ioa.ac.cn, wangj@mail.ioa.ac.cn, whb@mail.ioa.ac.cn.

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PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce el aprendizaje bayesiano disperso fuera de la red para la formación de haces desconectados, mejorando la resolución espacial para objetivos del mundo real. El método mejorado supera las limitaciones de las técnicas tradicionales para los patrones de haz con variación de desplazamiento y se dirige a las redes de muestreo.

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Área de la Ciencia:

  • Procesamiento de señales
  • Procesamiento de señales de matriz
  • Electromagnetismo computacional

Sus antecedentes:

  • La formación de haz desconectado (dCv) mejora la resolución espacial sin aumentar el tamaño de la matriz.
  • Los dCv tradicionales luchan con patrones de haz variantes de desplazamiento y objetivos que no están en las redes de muestreo.
  • La localización espacial precisa es crucial en varias aplicaciones de detección.

Objetivo del estudio:

  • Extender el aprendizaje bayesiano disperso fuera de la red (OGSBL) a la formación de haces desconvocados (dCv).
  • Para abordar las limitaciones de dCv relativas a los patrones de haz con variación de desplazamiento y a los objetivos fuera de la red.
  • Mejorar la resolución espacial y la precisión en las técnicas de formación de haces.

Principales métodos:

  • Modelo convolucional generalizado que incorpora el patrón de haz por ángulo.
  • Parametrización de las ubicaciones muestreadas en redes gruesas para reducir los errores de modelado.
  • Control del número de haces de salida para cubrir las regiones espaciales de interés para una convergencia más rápida.

Principales resultados:

  • El dCv propuesto mejorado con OGSBL maneja eficazmente los patrones de haz de variación de desplazamiento.
  • Se logra una localización precisa de los objetivos que no se encuentran en las redes de muestreo.
  • Los resultados de la simulación demuestran el buen rendimiento y la precisión del método.

Conclusiones:

  • La integración de OGSBL con dCv ofrece una solución robusta para mejorar la resolución espacial.
  • Este enfoque supera las limitaciones clave de la dCv convencional.
  • El método muestra un potencial significativo para aplicaciones avanzadas de formación de haces.