Electromagnetic Inversion Algorithm for Nonsparse Targets via CNN-Assisted Depth-First Multipath Matching Pursuit

Electromagnetic (EM) inversion refers to reconstructing the EM properties of unknown targets from measured scattered fields. In practical applications, various algorithms have been developed, especially those that rely on greedy pursuit (GP) within the compressive sensing framework. However, the GP-based algorithms still suffer from several intractable issues, such as poor priors of the pregiven sparsity index, insufficient robustness of generated single support, and possible failure when reconstructing nonsparse targets. A novel algorithm based on depth-first multipath matching pursuit (MMP-DF) assisted by a convolutional neural network (CNN) is proposed to overcome these issues. Specifically, the CNN is designed to predict the sparsity index, and MMP-DF is implemented to identify multiple promising supports for improving the likelihood of finding favorable support, and the Fourier basis is applied to transform the case of nonsparse targets into sparse ones. Theoretical analysis and numerical simulations show that, compared with the algorithm based on orthogonal matching pursuit, the proposed algorithm achieves improved and robust results in the case of nonsparse targets.