Initialized Iterative Reweighted Least Squares For Automatic Target Recognition

Keywords

Automatic Target Recognition; Compressed Sensing; Iterative Reweighted Least Squares; Stochastic Initialization

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.

Publication Date

12-14-2015

Publication Title

Proceedings - IEEE Military Communications Conference MILCOM

Volume

2015-December

Number of Pages

506-510

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/MILCOM.2015.7357493

Socpus ID

84959306682 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84959306682

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