VGG-16 optimized by bitterling fish optimization for classifying underwater target radiated noise signals

With the increasing demand for marine resource development, national security, and marine environmental monitoring, the importance of underwater target detection technology has become increasingly prominent. The underwater target radiated noise signal is the core basis for identifying underwater targets, and its accurate recognition is crucial. This paper proposes a classification method for underwater target radiated noise signals based on enhanced images and a bio-inspired algorithm. The underwater target radiated noise signals collected by the hydrophone are converted into enhanced images, and a convolutional neural network is established for image classification. By leveraging the powerful advantages of convolutional neural network in image processing, classification accuracy is improved. To enhance computational efficiency and classification accuracy, advanced bio-inspired optimization algorithms are used to optimize the hyperparameters of the convolutional neural network, providing a new approach for underwater target radiated noise signals classification and recognition.