Target Discrimination in Polarimetric ISAR Data using Robust Feature Vectors

Abstract

We study the robustness of features against aspect variability and target modification for the purpose of target discrimination using polarimetric 35 Ghz ISAR data. The data are obtained with the MEMPHIS radar and comprise ISAR data of 16 targets providing imagery at a resolution of about 20 cm resolution for a complete aspect angle range of 360 degrees. The data cover three classes of military targets (T72, ZSU and BMP) with several modifications. For the study we have composed feature vectors out of individual radiometric, geometric and polarimetric features extracted from the imagery. Using the feature vectors and a nearest neighbor classifier we have determined how well different targets classes and different target modifications can be separated. We have found that good discrimination results are obtained between the target classes but that no discrimination is obtained between the different modifications.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 2005
Accession Number
ADA471110

Entities

People

  • Bert Van Den Broek
  • Rob Dekker

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Aspect Angle
  • Classification
  • Databases
  • Detection
  • Detectors
  • Discrimination
  • Frequency
  • High Resolution
  • Information Science
  • Measurement
  • Scattering
  • Supervised Machine Learning
  • Target Discrimination
  • Target Recognition
  • Two Dimensional
  • Warning Systems

Readers

  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML