%0 Conference Paper %B Image Processing (ICIP), 2010 17th IEEE International Conference on %D 2010 %T Automatic target recognition based on simultaneous sparse representation %A Patel, Vishal M. %A Nasrabadi,N.M. %A Chellapa, Rama %K (artificial %K algorithm;feature %K based %K classification;iterative %K classification;learning %K Comanche %K data %K dictionary;matching %K extraction;image %K forward-looking %K infrared %K intelligence);military %K learning %K MATCHING %K matrix;dictionary %K measure;military %K methods;learning %K orthogonal %K pursuit %K pursuit;confusion %K recognition;class %K recognition;target %K representation;feature %K representation;sparse %K set;automatic %K signal %K similarity %K simultaneous %K sparse %K supervised %K systems;object %K target %K target;simultaneous %K tracking; %X In this paper, an automatic target recognition algorithm is presented based on a framework for learning dictionaries for simultaneous sparse signal representation and feature extraction. The dictionary learning algorithm is based on class supervised simultaneous orthogonal matching pursuit while a matching pursuit-based similarity measure is used for classification. We show how the proposed framework can be helpful for efficient utilization of data, with the possibility of developing real-time, robust target classification. We verify the efficacy of the proposed algorithm using confusion matrices on the well known Comanche forward-looking infrared data set consisting of ten different military targets at different orientations. %B Image Processing (ICIP), 2010 17th IEEE International Conference on %P 1377 - 1380 %8 2010/09// %G eng %R 10.1109/ICIP.2010.5652306