  In this paper, we present a spectrum monitoring framework for the detection
of radar signals in spectrum sharing scenarios. The core of our framework is a
deep convolutional neural network (CNN) model that enables Measurement Capable
Devices 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 the transmissions of multiple radar pulse waveforms, downlink LTE,
WLAN, and thermal noise. 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 model is then tested under
various SNR values, outperforming other models, such as spectrogram-based CNN
models.
