Reconstruction Algorithm of Compressed Sensing for Stepped-Frequency Continuous Wave Ground Penetrating Radar Based on Block Objects

Compressed sensing (CS) is of great significance to solving such problems as high sampling rate, huge storage pressure and long processing time in the process of stepped-frequency continuous wave ground penetrating radar (SFCW-GPR). Aiming at the problem that block objects can't meet the sparseness in the detecting area, and using the orthogonal basis for sparse processing of block objects to satisfy the sparsity condition, a new observation matrix that was suitable for block objects was formed by combining the dictionary matrix and sparse matrix. The sparse coefficients were solved by using the compressed sensing convex optimization algorithm. Finally, the reflection coefficients of block objects were obtained through sparse transformation of the sparse coefficients. The simulation results showed that the proposed method is feasible and has higher accuracy and resolution ratio compared with the compressed sensing reconstruction model without sparsity. © 2018, Editorial Department of Journal of Northeastern University. All right reserved.