Enhanced Multidimensional Nonlinear Correlation via Phase Reconstruction and Broad Learning for Distributed Fusion Detection of Weak Pulse Signals
Guardat en:
| Publicat a: | International Journal of Intelligent Systems vol. 2025 (2025) |
|---|---|
| Autor principal: | |
| Altres autors: | |
| Publicat: |
John Wiley & Sons, Inc.
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3202632585 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0884-8173 | ||
| 022 | |a 1098-111X | ||
| 024 | 7 | |a 10.1155/int/8827255 |2 doi | |
| 035 | |a 3202632585 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 163929 |2 nlm | ||
| 100 | 1 | |a Su, Liyun |u School of Sciences Chongqing University of Technology Chongqing 400054 China | |
| 245 | 1 | |a Enhanced Multidimensional Nonlinear Correlation via Phase Reconstruction and Broad Learning for Distributed Fusion Detection of Weak Pulse Signals | |
| 260 | |b John Wiley & Sons, Inc. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Machine learning | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Signal processing | ||
| 653 | |a Neural networks | ||
| 653 | |a Signal detection | ||
| 653 | |a Correlation | ||
| 653 | |a Sensors | ||
| 653 | |a Distributed sensor systems | ||
| 653 | |a Classification | ||
| 653 | |a Methods | ||
| 653 | |a Clutter | ||
| 653 | |a Algorithms | ||
| 653 | |a Reconstruction | ||
| 653 | |a Multisensor fusion | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Simulation | ||
| 700 | 1 | |a Long, Xuelian |u School of Sciences Chongqing University of Technology Chongqing 400054 China | |
| 773 | 0 | |t International Journal of Intelligent Systems |g vol. 2025 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3202632585/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3202632585/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3202632585/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |