Marine vessel classification based on passive sonar data: the cepstrum-based approach

Marine vessel classification is complicated by the variability in the radiated signal of the marine vessel because of changing machinery configuration for the same class of vessels. Further, the radiated signal of the marine vessel propagating towards a distant receiver undergoes random fluctuations in phase, amplitude and frequency. The ambient noise at the receiver will further complicate the authors´ classification problem. The shallow underwater channel, in particular, where these classification systems are more likely to operate presents the most challenges because of severe time-varying multi-path. Cepstral approaches are proposed in this study, including cepstral features and average cepstral features to augment existing feature sets that are mostly based on spectral analysis. Analytical studies have been supported by simulation experiments and tests on real ship recorded data. The cepstral features with cepstral liftering process is able to significantly reduce the multipath distortion effects of shallow underwater channel whereas the average cepstral feature is able to notably reduce the time-varying channel effects.