Complex active sonar targets recognition using variable length deep convolutional neural network evolved by biogeography-based optimizer

Due to heterogeneous sound propagation conditions and fluctuating ambient noises, conventional handcrafted feature extraction methods represent poor results and high complexity in underwater sonar wave recognition tasks. In order to address these shortcomings, this paper proposes a hybrid metaheuristic deep learning-based approach. However, model depth may vary under different underwater ocean conditions. The deeper the model, the greater the number of hyperparameters, challenging the search space. It is crucial to have an efficient algorithm that can obtain an accurate model in a reasonable time. Therefore, this paper proposes the Variable-Length Habitat Biogeography-Based Optimizer (VLHBBO) to tune the hyperparameters of a deep conventional neural network. Given that there is no appropriate dataset for training the proposed model, experimental underwater scattering measurement is conducted on several target and non-target objects of the same size in the east of the Persian Gulf and the west of the Oman Sea. Furthermore, this study uses the benchmark datasets obtained from the New Array Technology III program as test datasets. The performance of the proposed model is compared to other underwater target classifiers in terms of eight metrics. The classification results indicate that the proposed VLBBO-DCNN classifier can effectively classify underwater sonar waves into relevant categories.