volume | PIER Journals

Abstract

A novel dual compressive sensing (DCS) method is presented to resolve the limitations in computational accuracy and efficiency encountered by conventional DCS approaches during monostatic electromagnetic scattering analysis. Based on the connection between adaptive cross approximation (ACA) and generalized orthogonal matching pursuit (gOMP) in DCS, the integration of ACA and gOMP into the DCS framework is implemented. Specifically, ACA is employed to construct a deterministic measurement matrix by extracting row indexes containing critical information from the impedance matrix. This method reduces column correlation in the measurement matrix, enabling fewer rows to achieve comparable accuracy. Furthermore, the gOMP algorithm is adopted for signal recovery, leveraging its multi-column selection mechanism to better utilize the optimized information from ACA, thereby enhancing reconstruction accuracy and efficiency. Numerical analysis demonstrates that the proposed method achieves a significant enhancement in both accuracy and efficiency.

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Dr. Chenggang Wu

Professor Zhonggen Wang

Professor Wenyan Nie

Dr. Dai Dong

Dr. Yang Liu