Hydroacoustic Channel Analysis and Matching Technique Based on Feature Selection and Model Self-Evolution

Hydroacoustic communication technology plays a key role in many applications such as marine oil and gas exploration, marine pollution monitoring, underwater rescue missions and underwater submersible navigation. Although traditional hydroacoustic channel estimation techniques rely on a priori statistics, they are not sufficient to accommodate abrupt changes in channel state. In addition, these methods do not utilize the inherent sparsity of the channel, leading to sub-optimal estimation accuracy and efficiency. Addressing the complexity of the hydroacoustic communication environment and the sparsity of the channel, this study delves into the challenges of hydroacoustic channel feature selection. The core challenges include hydroacoustic channel feature extraction, automatic classification of channel features, and model self-evolution techniques trained on hydroacoustic data. Using modulation methods such as QPSK, OOK, 16QAM, and DQPSK, the selection accuracy of the preferred communication system exceeds 85% and the probability of correct judgment exceeds 84%. The results of this hydroacoustic channel feature extraction model bring hope for the development of underwater communication and provide a way to cultivate hydroacoustic communication technology with ownership of hydroacoustic channels.