Robust automatic target recognition using learning classifier systems

摘要

This work developed and demonstrated a machine learning approach for robust ATR. The primary innovation of this work was the development of an automated way of developing inference rules that can draw on multiple models and multiple feature types to make robust ATR decisions. The key realization is that this "meta learning" problem is one of structural learning, and that it can be conducted independently of parameter learning associated with each model and feature based technique. This was accomplished by using a learning classifier system, which is based on genetics-based machine learning, for the ill conditioned combinatorial problem of structural rule learning, while using statistical and mathematical techniques for parameter learning. This system was tested on MSTAR Public Release SAR data using standard and extended operation conditions. These results were also compared against two baseline classifiers, a PCA based distance classifier and a MSE classifier. The classifiers were evaluated for accuracy (via training set classification) and robustness (via testing set classification). In both cases, the LCS based robust ATR system performed well with accuracy over 99% and robustness over 80%.