The Rational Resolution Analysis: A Generalization of Multiresolution Analyses with Application to the Specific Emitter Identification Problem

Abstract

The rational resolution analysis (RRA) is introduced and developed as a generalization of the integer, dilation multiresolution analyses (MRA) developed by Mallat and Meyer. Rational dilation factors are achieved by relaxing the condition on MRAs that successive approximation spaces be embedded. Conditions for perfect reconstruction are discussed and it is shown that perfect reconstruction is possible with specific constraints on the scaling function: the scaling filter must have its roots on the unit circle. Furthermore, the required arrangement of the roots indicate the scaling function must be derived from a B-spline of some degree. It is proven the only compactly supported scaling function which satisfies these constraints is the Haar basis. An algorithmic approach to constructing p-dilation wavelets is presented. The frame properties along with adjoint wavelets of RRAs are is presented. It is shown the adjoint wavelets form a frame for V0 and that the corresponding decomposition is both stable and unique. The redundant representation of the detail coefficients is exploited as a solution to the specific emitter problem. Results demonstrate the RRA is far superior to the traditional MRA and wavepacket approaches when used as a feature extractor in Bayesian classification schemes.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1996
Accession Number
ADA338634

Entities

People

  • Bruce P. Anderson

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Classification
  • Data Sets
  • Electrical Engineering
  • Engineering
  • Feature Extraction
  • Frequency Response
  • Identification
  • Image Processing
  • Pattern Recognition
  • Plastic Explosives
  • Radar
  • Radar Pulses
  • Signal Processing
  • Test And Evaluation
  • Trees (Data Structures)

Readers

  • Approximation Theory.
  • Graph Algorithms and Convex Optimization.
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space