An Evaluation of SAR ATR Algorithm Performance Sensitivity to MSTAR Extended Operating Conditions

Abstract

Testing a SAR Automatic Target Recognition (ATR) algorithm at or very near its training conditions often yields near perfect results as we commonly see in the literature. This paper describes a series of experiments near and not so near to ATR algorithm training conditions. Experiments are setup to isolate individual Extended Operating Conditions (EOCs) and performance is reported at these points. Additional experiments are setup to isolate specific combinations of EOCs and the SAR ATR algorithm's performance is measured here also. The experiments presented here are a by-product of a DARPA/AFRL Moving and Stationary Target Acquisition and Recognition (MSTAR) program evaluation conducted in November of 1997. Although the tests conducted here are in the domain of EOCs, these tests do not encompass the real world (i.e., what you might see on the battlefield) problem. In addition to performance results this paper describes an evaluation methodology including the Extended Operating Condition concept, as well as, data; algorithm; and figures of merit. In summary, this paper highlights the sensitivity that a baseline Mean Squared Error (MSE) ATR algorithm has to various operating conditions both near and varying degrees away from the training conditions.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1998
Accession Number
ADA357055

Entities

People

  • Jeff Bradley
  • John C. Mossing
  • Timothy D. Ross

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Automatic
  • Depression Angles
  • Detection
  • Detectors
  • Identification
  • Machine Learning
  • Military Research
  • Radar
  • Recognition
  • Sensitivity
  • Standards
  • Synthetic Aperture Radar
  • Target Acquisition
  • Target Recognition
  • Training

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.