Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification

Randall W Klein

ISBN 10: 1025132785 / ISBN 13: 9781025132785

Edité par Creative Media Partners, LLC Mai 2025, 2025

A propos de cet article

Neuware - This work addresses various Open Systems Interconnection (OSI) Physical (PHY) layer mechanisms to extract and exploit RF waveform features ('fingerprints') that are inherently unique to specific devices and that may be used to provide hardware specific identification(manufacturer, model, and/or serial number). This is addressed by applying a Dual-Tree Complex Wavelet Transform (DT-CWT) to improve burst detection and RF fingerprint classification. A 'Denoised VT' technique is introduced to improve performance at lower SNRs, with denoising implemented using a DT-CWT decomposition prior to Traditional VT processing. A newly developed Wavelet Domain (WD) fingerprinting technique is presented using statistical WD fingerprints with Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classification.

N° de réf. du vendeur 9781025132785

Détails bibliographiques

• Titre: Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification
• Éditeur: Creative Media Partners, LLC Mai 2025
• Date de parution: 2025
• Langue: anglais
• ISBN à 10 chiffres: 1025132785
• ISBN à 13 chiffres: 9781025132785
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 234 L x 156 H x 10 P (en millimètres) 234 millimètres de largeur par 156 millimètres de hauteur par 10 millimètres de profondeur
• Poids: 390 grammes

A propos de ce titre

Synopsis

This work addresses various Open Systems Interconnection (OSI) Physical (PHY) layer mechanisms to extract and exploit RF waveform features ("fingerprints") that are inherently unique to specific devices and that may be used to provide hardware specific identification(manufacturer, model, and/or serial number). This is addressed by applying a Dual-Tree Complex Wavelet Transform (DT-CWT) to improve burst detection and RF fingerprint classification. A "Denoised VT" technique is introduced to improve performance at lower SNRs, with denoising implemented using a DT-CWT decomposition prior to Traditional VT processing. A newly developed Wavelet Domain (WD) fingerprinting technique is presented using statistical WD fingerprints with Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classification.

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