Copertina

Enhanced Multidimensional Nonlinear Correlation via Phase Reconstruction and Broad Learning for Distributed Fusion Detection of Weak Pulse Signals

Salvato in:
Pubblicato in:International Journal of Intelligent Systems vol. 2025 (2025)
Autore principale: Su, Liyun
Altri autori: Long, Xuelian
Pubblicazione:
John Wiley & Sons, Inc.
Soggetti:
Machine learning
Fault diagnosis
Signal processing
Neural networks
Signal detection
Correlation
Sensors
Distributed sensor systems
Classification
Methods
Clutter
Algorithms
Reconstruction
Multisensor fusion
Parameter estimation
Statistical analysis
Simulation
Accesso online:Citation/Abstract
Full Text
Full Text - PDF
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
Abstract:Due to the intricate chaotic environments encountered in distributed sensor applications, such as sea monitoring, machinery fault diagnosis, and EEG weak signal detection, neural networks often face insufficient data to effectively carry out detection tasks. In contrast to traditional machine learning models, a statistical approach employing multidimensional nonlinear correlation (MNC) exhibits an unparalleled signal pattern prediction capability and possesses a streamlined yet robust framework for signal processing. However, the direct application of MNC to weak pulse signal detection remains constrained. To surmount these challenges and achieve high-precision signal detection, we explore a novel MNC approach, integrating phase reconstruction and manifold broad learning, specifically tailored for distributed sensor fusion detection amidst chaotic noise. Initially, the distributed observational data undergoes phase space reconstruction, transforming it into fixed-size arrays. These reconstructed tuples are then processed through the high-dimensional sequence of manifold broad learning, serving as inputs for the nonlinear correlation module to extract spatiotemporal features. Subsequently, a MNC system augmented with a QRS detector layer is devised to predict and classify the presence of a weak pulse signal. This integrated MNC approach, combining phase reconstruction and broad learning, operates within an enhanced feature space of the source domain, realizing detection fusion across distributed sensors through a majority voting principle. Simulation studies and experiments conducted on sea clutter datasets demonstrate the efficacy and robustness of the proposed MNC method, leveraging phase reconstruction and manifold broad learning strategies, for distributed sensor weak pulse signal fusion detection within chaotic backgrounds.
ISSN:0884-8173
1098-111X
DOI:10.1155/int/8827255
Fonte:Advanced Technologies & Aerospace Database