For the acquisition of ocean observation information, the underwater acoustic signal is the only known medium that can propagate over long distances in water. However, the underwater acoustic field environment is highly time-varying and spatially variable, and ocean acoustic observation signals are mixed Gaussian, non-cooperative, and nonlinear. Therefore, feature extraction of underwater target signals has been a challenging research direction. The high-order domain feature extraction method of ocean acoustic observation signal is widely used because of its advantages of anti-Gaussian background noise and preservation of signal phase information. Firstly, this paper analyzes three aspects, including the characteristics of ocean acoustic observation signals, the limitations of second-order domain processing methods, and advantages of high-order domain processing methods. Then, this paper focuses on theoretical advantages and application areas of signal high-order domain feature extraction technology. The technical development and application performance of common feature extraction technology in the field of ocean acoustic observation signal processing by higher-order domain methods in recent years are analyzed in the paper, and the challenges and future trends of higher-order domain feature extraction technology in marine acoustic observation signal processing are discussed in the context of recent studies.

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In recent years, interest in aquaculture acoustic signal has risen since the development of precision agriculture technology. Underwater acoustic signals are known to be noisy, especially as they are inevitably mixed with a large amount of environmental background noise, causing severe interference in the extraction of signal features and the revelation of internal laws. Furthermore, interference adds a considerable burden on the transmission, storage, and processing of data. A signal recognition curve (SRC) algorithm is proposed based on higher-order cumulants (HOC) and a recognition-sigmoid function for feature extraction of target signals. The signal data of interest can be accurately identified using the SRC. The analysis and verification of the algorithm are carried out in this study. The results show that when the SNR is greater than 7 dB, the SRC algorithm is effective, and the performance improvement is maximized when the SNR is 11 dB. Furthermore, the SRC algorithm has shown better flexibility and robustness in application.