DocumentCode :
3712714
Title :
Initialized iterative reweighted least squares for automatic target recognition
Author :
Brian Millikan;Aritra Dutta;Nazanin Rahnavard;Qiyu Sun;Hassan Foroosh
Author_Institution :
Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, 32816, United States of America
fYear :
2015
Firstpage :
506
Lastpage :
510
Abstract :
Automatic target recognition is typically deployed on infrared focal plane arrays with high resolution, which could be costly. Due to the compressibility of infrared images, compressive sensing allows us to reduce the resolution requirements of a focal plane array while keeping the same target recognition ability. In this paper, we develop an iterative reweighted least squares algorithm with stochastically trained initial weights. Our simulations indicate that this method has higher automatic target recognition accuracy than conventional methods such as OMP, BP, and IRLS when applied to the U.S. Army Night Vision and Electronic Sensors Directorate (NVESD) dataset.
Keywords :
"Target recognition","Image coding","Signal processing algorithms","Image reconstruction","Matching pursuit algorithms","Image resolution","Sensors"
Publisher :
ieee
Conference_Titel :
Military Communications Conference, MILCOM 2015 - 2015 IEEE
Type :
conf
DOI :
10.1109/MILCOM.2015.7357493
Filename :
7357493
Link To Document :
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