Efficient Class-Specific Models for Autoregressive Processes with Slowly Varying Amplitude in White Noise

Abstract

This paper describes an efficient model to describe an autoregressive signal with slowly-varying amplitude in additive white Gaussian noise. Even a simple low-order autoregressive model becomes complicated by varying amplitude and additive white noise. However, by approximating the signal amplitude as piecewise-constant, an efficient filtering approach can be applied in order to compute the maximum likelihood estimate for the entire data record. The model is efficient both in terms of having a compact set of parameters and in the computational sense. Simulation results are provided. The algorithm has applications in signal modeling for underwater acoustic signals, particularly active wideband signals such as explosive sources.

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Document Details

Document Type
Technical Report
Publication Date
Jul 13, 2007
Accession Number
ADA494495

Entities

People

  • Paul Baggenstoss

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Amplitude
  • Computational Science
  • Estimators
  • Frequency Domain
  • Gaussian Processes
  • Information Science
  • Mathematical Filters
  • Mathematical Models
  • Maximum Likelihood Estimation
  • Models
  • Noise
  • Power Spectra
  • Stationary Processes
  • Statistical Algorithms
  • White Noise

Fields of Study

  • Engineering

Readers

  • Acoustical Oceanography.
  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Statistical inference.