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Diagramas de Persistencia de Picos para Estimación de Señales Basada en Forma

Woo Min Kim1, Sutanoy Dasgupta2, Pavan Turaga3

  • 1Department of Statistics, Florida State University, Tallahassee, FL 32306, USA.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society
|January 16, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo método de estimación de señales que utiliza características topológicas y geométricas de los datos. El enfoque de alineación elástica penalizada (PESA) mejora la precisión para señales con ruido aditivo y de deformación.

Palabras clave:
deformación temporal dinámicaalineación elásticapersistencia de picosestimación de formaseñal con restricción de formaestimación de señal

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Área de la Ciencia:

  • Procesamiento de Señales
  • Análisis de Datos
  • Topología Computacional

Sus antecedentes:

  • La estimación de señales a partir de datos ruidosos es un desafío central.
  • Los métodos existentes se basan en elecciones de modelos y criterios específicos.
  • Existe la necesidad de estimadores robustos que manejen tipos de ruido complejos.

Objetivo del estudio:

  • Desarrollar un marco innovador de estimación de señales utilizando características de datos topológicas y geométricas.
  • Introducir un diagrama de persistencia de picos (PPD) para el análisis de la forma de la señal.
  • Proporcionar un estimador robusto para señales con ruido aditivo y de deformación.

Principales métodos:

  • Aprovechamiento del marco de alineación elástica penalizada (PESA).
  • Utilización de diagramas de persistencia de picos (PPD) para estimar la forma de la señal (picos/valles).
  • Empleo de optimización con restricciones de forma para la estimación de señales.

Principales resultados:

  • El enfoque PESA equilibra el promediado de señales y la alineación elástica.
  • Se presenta un procedimiento computacionalmente eficiente para el método propuesto.
  • Demostró un rendimiento superior frente a técnicas de vanguardia en simulaciones y datos del mundo real.

Conclusiones:

  • El marco PESA propuesto ofrece un avance significativo en la estimación de señales.
  • Efectivo para analizar conjuntos de datos complejos como las curvas de tasas de COVID y consumo de electricidad.
  • Destaca la utilidad de las características topológicas en el procesamiento de señales.