Underwater Noise Target Recognition Based on Sparse Adversarial Co-Training Model with Vertical Line Array

The automatic identification of underwater noncooperative targets without label records remains an arduous task considering the marine noise interference and the shortage of labeled samples. In particular, the data-driven mechanism of deep learning cannot identify false samples, aggravating the difficulty in noncooperative underwater target recognition. A semi-supervised ensemble framework based on vertical line array fusion and the sparse adversarial co-training algorithm is proposed to identify noncooperative targets effectively. The sound field cross-correlation compression (SCC) feature is developed to reduce noise and computational redundancy. Starting from an incomplete dataset, a joint adversarial autoencoder is constructed to extract the sparse features with source depth sensitivity, aiming to discover the unknown underwater targets. The adversarial prediction label is converted to initialize the joint co-forest, whose evaluation function is optimized by introducing adaptive confidence. The experiments prove the strong denoising performance, low mean square error, and high separability of SCC features. Compared with several state-of-the-art approaches, the numerical results illustrate the superiorities of the proposed method due to feature compression, secondary recognition, and decision fusion.