Multicollinearity Diagnostics on Cyclostationary Features

Abstract

Cyclostationary features such as high-order moments and cumulants are often applied in detecting and classifying digitally modulated signals. However, these predictor variables may not be mutually uncorrelated, raising the concern that potential multicollinearity may lead to redundant information and significantly affect quality prediction. This report explores machine learning and statistical methods for detecting and mitigating multicollinearity in cyclostationary features. Our study examines features corresponding to synthetic electromagnetic waveforms of nine modulation types over nine levels of signal-to-noise. The empirical results indicate that the Variance Inflation Factor (VIF) method and the Eigendecomposition of the predictor correlation matrix are effective in detecting multicollinearity. Furthermore, employing feature transformation using the Principal Component Analysis (PCA) technique allows us to identify principal components that are less influential in the performance of prediction models.

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Document Details

Document Type
Technical Report
Publication Date
Nov 07, 2022
Accession Number
AD1184366

Entities

People

  • Anthony S. Tai

Organizations

  • Naval Surface Warfare Center Crane Division

Tags

Communities of Interest

  • Autonomy
  • Sensors

DTIC Thesaurus Topics

  • Carrier Frequencies
  • Data Processing
  • Data Science
  • Deep Learning
  • Detection
  • Dimensionality Reduction
  • Eigenvalues
  • Eigenvectors
  • Electrical Engineering
  • Factor Analysis
  • Feature Selection
  • Frequency
  • Information Science
  • Learning
  • Machine Learning
  • Radar
  • Signal Processing
  • Surface Warfare
  • Target Detection

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Radio communications and signal processing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks