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Mode Selection in Cognitive Radar Networks
Cognitive Radar Networks, which were popularized by Simon Haykin in 2006, have been proposed to address limitations with legacy radar installations.
Mode Selection and Target Classification in Cognitive Radar Networks
The goal of the network is to learn over many target tracks both the characteristics of the targets as well as the optimal action choices for each type of target.
Open Set Wireless Signal Classification: Augmenting Deep Learning with Expert Feature Classifiers
To address unknown signals, we propose an open set hybrid classifier, which combines deep learning and expert feature classifiers to leverage the reliability and explainability of expert feature classifiers and the lower computational complexity of deep learning classifiers.
Open Set Wireless Standard Classification Using Convolutional Neural Networks
The closed set classifier achieves an average accuracy of 94. 5% for known signals with SNR's greater than 0 dB, but by design, has a 0% accuracy detecting signals from unknown classes.
