Adaptive and Fixed Wavelet Features for Narrowband Signal Classification

Abstract

The application of the multiresolution analysis developed by Mallat to signal classification by Pati and Krishnaprasad and Szu, et al, is further explored in this thesis. Several different wavelet based feature extraction and classification systems are developed and implemented. Methods which rely on the traditional dyadic wavelet decomposition and on the adaptive wavelet representation are presented. Each of the classification systems is implemented for a labeled data set of narrowband signals. Finally, classification results on the full data set and on low frequency Fourier coefficients are provided as baseline comparisons for our work.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1995
Accession Number
ADA305961

Entities

People

  • Anthony J. Pohl

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Amplitude Modulation
  • Data Reduction
  • Data Sets
  • Demodulation
  • Dimensionality Reduction
  • Feature Extraction
  • Frequency
  • Frequency Modulation
  • Machine Learning
  • Modulation
  • Narrowband
  • Neural Networks
  • Pattern Recognition
  • Probability
  • Signal Processing

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Regression Analysis.

Technology Areas

  • AI & ML