Characterizing Signals Using Nonlinear Dynamical Models

Abstract

Develop new Sonar processing and classification methods based on direct estimation of nonlinear dynamical models from scalar signals. This method has the possibility of exploiting both linear and nonlinear signal correlations, as well as deterministic causal information. The goal is to develop numerically efficient, robust classifiers with significantly improved performance for Sonar transients, broadband, and VLF signatures, which can be implemented on existing Navy processing platforms.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 1998
Accession Number
ADA541966

Entities

People

  • Jim Kadtke

Organizations

  • University of California, San Diego

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Acoustic Signals
  • Active Sonar
  • Classification
  • Coefficients
  • Data Analysis
  • Data Sets
  • Detection
  • Detectors
  • Differential Equations
  • Discrimination
  • Information Operations
  • Machine Learning
  • Scientists
  • Signal Processing
  • Sonar
  • Teamwork
  • User Friendly

Fields of Study

  • Engineering

Readers

  • Acoustical Oceanography.
  • Calculus or Mathematical Analysis
  • Computational Modeling and Simulation