volume | PIER Journals

Abstract

Most of the works on sparse array synthesizing via the compressed sensing (CS) approach assume that the active elements exactly lie on the predefined grids. In fact, grid-mismatch error is unavoidable when an array aperture is discretized into grids, and the synthesizing results largely depend on the density of the grids. To overcome this limitation, an innovative off-grid CS approach is proposed for jointly estimating the excitations and positions of array elements. The synthesis problem is specifically formulated using a ridge regression model based on dynamic grids. The candidate positions of elements are treated as variables rather than constants predefined by discretization. Numerical experiments are conducted to validate the effectiveness and flexibility of the proposed method in realizing several maximally sparse arrays meeting the targeted patterns, i.e., the focused and shaped beam patterns of 1-D and 2-D arrays.

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Dr. Xiaowen Zhao

Prof. Yunhua Zhang