Classification of underwater acoustic signals using multi-classifiers

Recent advancements in maritime technologies, pure oceanic research and military techniques related to underwater acoustics have introduced us to a new class of problems in digital signal processing. Apart from acoustic measurements, underwater classification to identify signatures poses a challenging task at hand due to typical profile of anthropogenic noise which is very complex. Moreover, unavailability of large relevant datasets makes it hard to achieve high accuracy in classification. Underwater acoustic channel is a dynamic one that not only includes noise due to fish and marine life but also that from commercial human activity and military operations. This work shows that a single classification approach is not enough to classify signals with very high accuracy. Several techniques of underwater acoustic classification using objects' unique acoustic signature have been investigated. This classification process using acoustic signatures is divided into two stages. In the first stage, preprocessing and feature extraction needs to be performed on the given dataset — which typically includes a variety of marine life and anthropogenic signals. In the classification stage, multiple techniques including Naive Bayesian, K nearest neighbors, Artificial neural networks, Support vector machines and Hidden Markov Models have been performed to evaluate classification results. The shortcomings of each classifier are discussed thoroughly in this paper. The main challenges for the robust and fast classification of underwater signals are also described to mitigate them in future. Moreover, open research issues are also discussed, and possible solution approaches are outlined.