Complex Bayesian group Lasso for defect imaging with guided waves

The defect imaging based on guided wave provides an intuitive way for defect localization. Recently, sparse representation methods based on the damage sparsity assumption have been developed for defect imaging, where few sensors are used in these methods. However, these sparse imaging methods need repeatedly tuning the regularization parameter to obtain a good imaging performance. In this paper, an adaptive method based on complex Bayesian group Lasso is developed for localizing the damage. A group Lasso model is constructed to represent the defect imaging problem, and formulated by a sparse Bayesian learning (SBL) framework, where a hierarchical model of a Laplace prior is built to represent the group Lasso regularization. Estimations of the model variables are derived by using variational inference. In the proposed method, the model parameters are automatically updated without needing priori information. The effectiveness of the proposed method is verified by analyzing an experimental data.