Clutter Rejection Using Eigenspace Transformation

Abstract

The goal of our research is to develop an effective and efficient duller rejector with the use of an eigenspace transformation and a multilayer perception (MLP) that can be incorporated into an automatic target recognition (ATR) system. An eigenspace transformation is used for feature extraction and dimensionality reduction. The transformations considered in this research are principal component analysis (PCA) and the eignespace separation transform (EST). We fed the result of the eigenspace transformation to an MLP that predicts the identity of the input, which is either a target or clutter. Cur proposed clutter rejector was tested on two huge and realistic datasets of second generation forward-looking infrared (FLIR) imagery for the Comanche helicopter. In general, both the PCA and EST methods performed satisfactorily with minor differences. The EST method performed slightly better when a smaller amount of transformed data were fed to the MLP, or when the positive and negative EST eigentargets were used together.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1999
Accession Number
ADA368295

Entities

People

  • Lipchen A. Chan
  • Nasser M. Nasrabadi

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Demographic Cohorts
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Extraction
  • Factor Analysis
  • False Alarms
  • Feature Extraction
  • Image Processing
  • Information Science
  • Machine Learning
  • Military Research
  • Recognition
  • Target Recognition
  • Training

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Materials Science and Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML