  In this paper, we optimize a faster region-based convolutional neural network
(FRCNN) for 1-dimensional (1D) signal processing and electromagnetic spectrum
sensing. We target a cluttered radio frequency (RF) environment, where multiple
RF transmission can be present at various frequencies with different
bandwidths. The challenge is to accurately and quickly detect and localize each
signal with minimal prior information of the signal within a band of interest.
As the number of wireless devices grow, and devices become more complex from
advances such as software defined radio (SDR), this task becomes increasingly
difficult. It is important for sensing devices to keep up with this change, to
ensure optimal spectrum usage, to monitor traffic over-the-air for security
concerns, and for identifying devices in electronic warfare. Machine learning
object detection has shown to be effective for spectrum sensing, however
current techniques can be slow and use excessive resources. FRCNN has been
applied to perform spectrum sensing using 2D spectrograms, however is unable to
be applied directly to 1D signals. We optimize FRCNN to handle 1D signals,
including fast Fourier transform (FFT) for spectrum sensing. Our results show
that our method has better localization performance, and is faster than the 2D
equivalent. Additionally, we show a use case where the modulation type of
multiple uncooperative transmissions is identified. Finally, we prove our
method generalizes to real world scenarios, by testing it over-the-air using
SDR.
