Abstract
This paper deals with termite detection in non-favorable SNR scenarios via signal processing using higher-order statistics. The results could be extrapolated to all impulse-like insect emissions; the situation involves non-destructive termite detection. Fourth-order cumulants in time and frequency domains enhance the detection and complete the characterization of termite emissions, non-Gaussian in essence. Sliding higher-order cumulants offer distinctive time instances, as a complement to the sliding variance, which only reveal power excesses in the signal; even for low-amplitude impulses. The spectral kurtosis reveals non-Gaussian characteristics (the peakedness of the probability density function) associated to these non-stationary measurements, specially in the near ultrasound frequency band. Contrasted estimators have been used to compute the higher-order statistics. The inedited findings are shown via graphical examples.
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