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

Faster Region-Based CNN Spectrum Sensing and Signal Identification in Cluttered RF Environments