Super-resolution Underwater Acoustic Imaging Based on Sparse Bayesian Learning

In this paper, the sparse Bayesian learning (SBL) theory and underwater acoustic imaging model are the core, and the underwater acoustic imaging model and algorithm based on sparse Bayesian learning are introduced for the purpose of improving the resolution of underwater acoustic imaging. Focusing on the underwater acoustic imaging model, firstly, the echo signal model based on the bright spot model is established, and the observation matrices of the near-field and far-field targets are established according to the discriminant conditions of the near-field and far-field respectively. Secondly, the sparse Bayesian learning theory is introduced, and the obtained SBL algorithm is combined with the underwater acoustic imaging system to explain the sparsity in the system, and it can be seen that the accuracy of the SBL algorithm is theoretically better than that of the traditional algorithm. Finally, the derived SBL algorithm is combined with the specific underwater acoustic imaging parameters for simulation. According to the simulation results, the SBL algorithm can significantly improve the imaging resolution, and because it uses probabilistic information to quickly update the hyperparameters, it can complete the signal reconstruction with high performance, and has excellent noise immunity and universality.