Abstract

Automated identification and classification of short-duration oceanic signals obtained from passive sonar is a complex problem because of the large variability in both temporal and spectral characteristics even in signals obtained from the same source. This paper presents the design and evaluation of a comprehensive classifier system for such signals. We first highlight the importance of selecting appropriate signal descriptors or feature vectors for high-quality classification of realistic short-duration oceanic signals. Wavelet-based feature extractors are shown to be superior to the more commonly used autoregressive coefficients and power spectral coefficients for this purpose. A variety of static neural network classifiers are evaluated and compared favorably with traditional statistical techniques for signal classification. We concentrate on those networks that are able to tune out irrelevant input features and are less susceptible to noisy inputs, and introduce two new neural-net...

Citations