Diagnosis method for defective array elements based on compressive sensing

As the number of elements of the array increases, huge sampling data, long measurement time and high computation costs are several features of existing array fault diagnosis methods. Therefore, a diagnosis approach based on Compressive Sensing is investigated in this paper. The proposed method utilizes the sparsity of the number of failed elements and the sparse signal derives from difference incentives of reference array and the array under test. The criterion for grid division of the measurement matrix is designed according to the target direction in the spatial domain and a small number of measurement data are then obtained in the far field radiation pattern via a random under-sampling strategy. The Parallel Coordinate Decent Algorithm is used to implement fault diagnosis by reconstructing this sparse signal. Theoretical analysis as well as simulation results indicate that the proposed method not only reduces the amount of spatial sampling data, truncates the diagnosis time and abates the computational complexity significantly, but also improves the accuracy of recovered information on defective elements.