Deep learning cognitive radar for micro UAS detection and classification

摘要

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 function (SCF) that is noise resilient. In the experiment conducted, micro UASs are clamped to be immobile while propellers are on motion. Doppler effects caused by propeller motions of UASs are considered. By employing our binarized DBN, the computationally costly 91600 floating point multiplication operations required in the original DBN are represented by using zero computational cost no connections, simple connections, negation operations, bit-shifting operations, and bit-shifting with negation operations. In the simulation section, we show that the proposed system gives more than 90% accuracy in detecting the micro UASs in the environments with SNR ≥ -3 dB AWGN noise. Furthermore, the classification accuracy of different micro UASs remains more than 90% for environments with SNR ≥ 0 dB AWGN noise.