Abstract
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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">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.
Highlights
R EAL-aperture radar can obtain the target information of forward-looking area through antenna scanning, benefiting for autonomous landing, autopilot, and topographic mapping and many other applications [1]–[4]
We found that the coefficient matrix, which needs to be inversed has an approximate Toeplitz structure, which provides the possibility for fast inversion
The acceleration strategy adopted in this article realizes the fast inversion of coefficient matrix through GS decomposition, which reduces the computational complexity of matrix inversion
Summary
R EAL-aperture radar can obtain the target information of forward-looking area through antenna scanning, benefiting for autonomous landing, autopilot, and topographic mapping and many other applications [1]–[4]. Previous researchers have proposed many superresolution methods to improve azimuth resolution in forward-looking imaging [11]. Considering simultaneously improving resolution and preserving target contour, a TV-sparse based multiconstraint deconvolution method was proposed in our previous literature [25]. ZHANG et al.: SUPERRESOLUTION OF RADAR FORWARD-LOOKING IMAGING BASED ON ACCELERATED TV-SPARSE METHOD. A GS representation based fast TV-sparse (FTVsparse) method is proposed to achieve real-time superresolution imaging and preserve the contour of targets. The azimuth signal of radar forward-looking imaging is analyzed, and the azimuth echo is modeled as a convolution of antenna pattern and targets distribution Considering both the resolution improvement and contour preservation of targets, the sparse and TV combination constraints are introduced in the framework of regularization, converting the superresolution problem into a convex optimization problem.
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