Robust automatic target recognition using learning classifier systems.
Addressing the challenge of robust ATR, this work developed and demonstrated a machine learning approach based on Learning Classifier Systems. 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 more 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, and can also effectively draw on the strengths of all such techniques. This was accomplished by using robust, 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%.
|Title:||Robust automatic target recognition using learning classifier systems|
|Keywords:||machine learning, evolutionary computation, automatic target recognition|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
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