Adaptive target recognition

Bir Bhanu, Yingqiang Lin, Grinnell Jones, Jing Peng

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Target recognition is a multilevel process requiring a sequence of algorithms at low, intermediate and high levels. Generally, such systems are open loop with no feedback between levels and assuring their performance at the given probability of correct identification (PCI) and probability of false alarm (Pf) is a key challenge in computer vision and pattern recognition research. In this paper, a robust closed-loop system for recognition of SAR images based on reinforcement learning is presented. The parameters in model-based SAR target recognition are learned. The method meets performance specifications by using PCI and Pf as feedback for the learning system. It has been experimentally validated by learning the parameters of the recognition system for SAR imagery, successfully recognizing articulated targets, targets of different configuration and targets at different depression angles.

Original languageEnglish
Pages (from-to)289-299
Number of pages11
JournalMachine Vision and Applications
Volume11
Issue number6
DOIs
StatePublished - Apr 2000

Keywords

  • Parameter learning
  • Reinforcement learning
  • Target recognition

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