A Multi-agent UAV Swarm for Automatic Target Recognition

2006 ◽  
pp. 80-91 ◽  
Author(s):  
Prithviraj Dasgupta ◽  
Stephen O’Hara ◽  
Plamen Petrov
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Uav Swarm ◽  
Multi Agent

Multi-agent systems and neural networks for automatic target recognition on air images

2000 ◽  
Author(s):  
Roger F. Cozien ◽  
Christophe Rosenberger ◽  
Partrick Eyherabide ◽  
Joaquim Rossettini ◽  
Arnaud Ceyrolle
Keyword(s):  
Neural Networks ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

Feature and Extractor Evaluation Concepts for Automatic Target Recognition (ATR)

1995 ◽  
Author(s):  
Timothy D. Ross ◽  
Lori A. Westerkamp ◽  
David A. Gadd ◽  
Robert B. Kotz
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition

Application of Decision Analysis to Automatic Target Recognition Programmatic Decisions

2002 ◽  
Author(s):  
William K. Klimack ◽  
Christopher B. Bassham ◽  
Kenneth W. Bauer ◽  
Jr
Keyword(s):  
Decision Analysis ◽  
Target Recognition ◽  
Automatic Target Recognition

scholarly journals SAR Target Recognition via Meta-Learning and Amortized Variational Inference

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5966
Author(s):  
Ke Wang ◽  
Gong Zhang
Keyword(s):  
Target Recognition ◽  
Probability Distributions ◽  
Automatic Target Recognition ◽  
Variational Inference ◽  
Training Data ◽  
Superior Performance ◽  
Small Data ◽  
Meta Learning ◽  
Radar Automatic Target Recognition ◽  
Global Parameters

The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.

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