Feature extraction and classification of deep-sea mobile underwater acoustic channels

The reliable acoustic path (RAP) is one of the crucial channels for deep-sea sound propagation, which is affected weakly by the interface and has lower transmission loss, enabling long-distance communication. However, RAP-based deep-sea acoustic communication may face channel model mismatch issues. In order to analyze the dynamic characteristics of spatial-temporalvariability channels, deep-sea mobile underwater acoustic channel measurement experiments were conducted. This work proposes a deep learning method based on multi-dimensional properties to classify deep-sea channels. Specifically, the sound ray convergence zone leads to a complex multipath structure and severe delay spread in the RAP channel. The fuzzy c-means (FCM) algorithm is used for multipath clustering to extract accurate channel features, and then the Markov chain (MC) is introduced to track the evolution characteristics of multipath clusters. Finally, the coupling features of channel time-variant impulse response (TVIR) and multi-dimensionalstatistical properties are used as the input of convolutional neural networks (CNNs) to obtain the quantitative evaluation index as the channel classification to build a channel feature dataset for underwater mobile platforms. This dataset can effectively assist in identifying deep-sea mobile channels and promote the development of adaptive underwater acoustic communication systems on mobile platforms.