Initialized Iterative Reweighted Least Squares for Automatic Target Recognition

被引：0

作者：

Millikan, Brian ^{[1
,2
]}

Dutta, Aritra ^{[3
]}

Rahnavard, Nazanin ^{[1
]}

Sun, Qiyu ^{[3
]}

Foroosh, Hassan ^{[1
]}

机构：

[1] Univ Cent Florida, Dept Elect Engn & Comp Sci, Orlando, FL 32816 USA

[2] Lockheed Martin Corp Missiles & Fire Control, Orlando, FL 32819 USA

[3] Univ Cent Florida, Dept Math, Orlando, FL 32816 USA

来源：

2015 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM 2015) | 2015年

基金：

美国国家科学基金会;

关键词：

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

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：506 / 510

页数：5

共 27 条

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