Modeling ATR processes to predict their performance by using invariance, robustness and self-refusal approach

Document Type

Conference Proceeding

Department or Administrative Unit

Computer Science

Publication Date



A tremendous variety of types of targets, sensors, and environments is a great challenge for modern ATR systems, applications, and technologies. This makes extensive large-scale experimentation to evaluate performance of Automated Target Recognition (ATR) systems nearly impossible and motivates the proposed approach and algorithms to overcome this problem by using predictive modeling. This paper presents a methodology of ATR performance prediction called Predictive Modeling of Invariance and Robustness (PMIR). The key idea is to use parameters of ATR algorithms for predicting ATR performance. The levels of robustness and invariance of parameters are used here as predictive indicators of ATR performance along with self-refusal capabilities of the algorithms.


This article was originally published in 2009 12th International Conference on Information Fusion. The full-text article from the publisher can be found here.

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2009 12th International Conference on Information Fusion


© 2009 ISIF