Convolutional Neural Networks for Radar Detection
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
MLPs for detecting radar targets in gaussian clutter
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Low complexity MLP-based radar detector: influence of the training algorithm and the MLP size
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
MLP-based radar detectors for Swerling 1 targets
Pattern Recognition and Image Analysis
Approximating the Neyman-Pearson detector for swerling I targets with low complexity neural networks
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Neural network detectors for composite hypothesis tests
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
NN-Based detector for known targets in coherent weibull clutter
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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We optimize a neural network applied to binary detection such as those found in radar or sonar. Topics about designing the structure, training procedure and evaluating the performance, are discussed. The detector optimization is based on the use of a criterion function that yields a solution significantly superior to the typical sum-of-square-error. Using a modeled input, its performance is evaluated by Monte Carlo trials. As a result, detection curves are compared with the theoretical optimum ones (Neyman-Pearson detectors). For the model, and despite of the blind learning of the neural network, its performance is very close to optimal.