| Summary: | Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging. However, due to the existence of matrix inversion, it suffers from high computational complexity, which restricts the ability of radar real-time imaging. In this article, an Gohberg-Semencul (GS) decomposition-based fast TV-sparse (FTV-sparse) method is proposed to reduce the computational complexity of TV-sparse method. The acceleration strategy utilizes the low displacement rank features of Toeplitz matrix, realizing fast matrix inversion by using a GS representation. It reduces the computational complexity of traditional TV-sparse method from O(N<sup>3</sup>) to O(N<sup>2</sup>), benefiting for improvement of the computing efficiency. The simulation and experimental data processing results show that the proposed FTV-sparse method has almost no resolution loss compared with the traditional TV sparse method. Hardware test results show that the proposed FTV-sparse method significantly improves the computational efficiency of TVsparse method.
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