The paper “Spectrum Monitoring for Radar Bands using Deep Convolutional Neural Networks”,was accepted for the IEEE GLOBECOM 2017 conference (4-8 December, 2017, Singapore)
With this paper, TCD presents a spectrum monitoring framework for detection of radar emitters in spectrum sharing scenarios. The core of the framework is a deep convolutional neural network (CNN) model that enables Measurement Capable Devices (MCDs) to identify the presence of radar signals in the radio spectrum, even when these signals are overlapped with other sources of interference, such as commercial LTE and WLAN. We collected a large dataset of RF measurements, which include transmissions of multiple radar pulse waveforms, LTE and WLAN. We propose a pre-processing data representation that leverages the amplitude and phase shifts of the collected samples. This representation allows our CNN model to achieve a classification accuracy of 99.6% on our testing dataset. The trained CNN is then tested under various SNR values, outperforming other models, such as spectrogram-based CNN models.
Our colleagues will attend the event and disseminate ORCA.