Comparison of automatic target recognition system performance with full- and reduced-resolution correlators

1999 ◽  
Vol 38 (23) ◽  
pp. 5014
Author(s):  
Paul C. Miller
Keyword(s):  
System Performance ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition

Dynamic range compression deconvolution for enhancement of automatic target recognition system performance

2008 ◽  
Author(s):  
Bahareh Haji-saeed ◽  
Jed Khoury ◽  
W. D. Goodhue ◽  
Charles L. Woods ◽  
John Kierstead
Keyword(s):  
System Performance ◽  
Dynamic Range ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Dynamic Range Compression

Dynamic range compression deconvolution for enhancement of automatic target recognition system performance

2008 ◽  
Author(s):  
Bahareh Haji-Saeed ◽  
Jed Khoury ◽  
W. D. Goodhue ◽  
Charles L. Woods ◽  
John Kierstead
Keyword(s):  
System Performance ◽  
Dynamic Range ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Dynamic Range Compression

scholarly journals Hierarchical Recognition System for Target Recognition from Sparse Representations

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Zongyong Cui ◽  
Zongjie Cao ◽  
Jianyu Yang ◽  
Hongliang Ren
Keyword(s):  
Deep Learning ◽  
Deep Structure ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Feature Representation ◽  
Small Data ◽  
Learning Method ◽  
Classification Problems ◽  
Restricted Boltzmann Machines

A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants,L1-RNM,L2-RBM, andL1/2-RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.

A distributed automatic target recognition system using multiple low resolution sensors

2008 ◽  
Author(s):  
Zhanfeng Yue ◽  
Pramod Lakshmi Narasimha ◽  
Pankaj Topiwala
Keyword(s):  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Low Resolution

Automatic target recognition system using high-resolution radar

1996 ◽  
Author(s):  
Steven P. Jacobs ◽  
Joseph A. O'Sullivan ◽  
Mohammad Faisal ◽  
Donald L. Snyder
Keyword(s):  
High Resolution ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
High Resolution Radar

Use of the mean-square-error matching metric in a model-based automatic target recognition system

1998 ◽  
Author(s):  
Stephen A. Stanhope ◽  
Eric R. Keydel ◽  
Wayne D. Williams ◽  
Vasik G. Rajlich ◽  
Russell Sieron
Keyword(s):  
Mean Square Error ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Mean Square ◽  
Model Based ◽  
The Mean
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