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In this paper, we propose low-complexity binarized deep belief network (DBN) based deep learning approach along with noise resilient spectral correlation function as a feature characterization mechanism for automated modulation classification (AMC). Through simulation results, we have shown the detection accuracy of the proposed method is above than 90% when the channel SNR ≥ 0 dB and classification...
In this paper, we propose an intelligent cognitive radar system for detecting and classifying the micro unmanned aerial systems (micro UASs). In this system, we design a low-complexity binarized deep belief network (DBN) classifier that recognizes the signature patterns generated by using a Doppler radar based solution. To generate the distinguishable patterns, our work employs the spectral correlation...
In this paper, a radar sensor is proposed for the automated detection and classification of micro unmanned aerial systems (UASs), using Doppler signatures and their spectral correlation functions (SCFs). Our proposed system effectively detects and identifies UASs (within the radar beam width) by employing a Deep Belief Network (DBN) to classify the SCF signature patterns. The proposed system is experimentally...
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