Abstract
Spectrograms are a common form of time-frequency representation of wireless communication signals. In many practical scenarios spectrograms need to be processed to identify accurately the time-frequency region occupied by each individual radio transmission, which in this work is referred to as Signal Area (SA). Several methods have been proposed in the literature for Signal Area Estimation (SAE), however their performance degrades significantly in the low SNR regime. In this context, this work proposes a novel approach for SAE based on the use of two well-known techniques from the field of image processing, namely edge detection and flood fill. Edge detection is first employed to identify the edges of potential SAs and flood fill is then used to fill the area inside the estimated edges in order to produce a more accurate estimation of the SAs present in a spectrogram. The performance of three popular edge detection methods (gradient magnitude, Laplacian of Gaussian and Canny) is assessed both with simulations and experimental data. The obtained results show that the proposed strategy can improve significantly the performance of existing SAE methods in the low SNR regime (with estimation accuracy improvements up to 38-45% within the SNR interval from −20 dB to −10 dB) when used as a pre/post-processing stage, thus improving their sensitivity and effectively extending their overall SNR range of operation.