Passive moving target classification via spectra multiplication method

Traditional feature extractions, such as mel-frequency cepstral coefficients (MFCCs), are susceptible to acoustic channel effects, reverberation, and additive environmental noises when applied to passive moving target classification in underwater environment. A spectra multiplication method (SMM) is proposed in this letter to replace the estimated spectrum of MFCCs. SMM suppresses the time-variant noise, and remarkably improves the discriminability of features with historical signals. Compared with traditional MFCCs, the proposed method shows consistent performance improvements in experiments with measured data under different parameter settings. The effect of multiplication order of SMM on classification accuracy has also been discussed. Since the SMM is generalized to a filter-based view, the relation between cutoff frequency and multiplication order is given. The change of classification accuracy induced by the multiplication order is in accord with the variation of cutoff frequency when designed by IIR filters.