Abstract
The problem of low azimuth resolution has restricted the applicability for radar forward-looking imaging in practice. In this paper, a sparse with fast majorization-minimization (SFMM) superresolution algorithm was proposed to realize fast superresolution imaging of sparse targets in radar forward-looking area. First, we analyzed the azimuth signal of the radar forward-looking area and modeled the azimuth signal as a convolution of antenna pattern and targets distribution. Second, the superresolution problem was converted into an L 1 regularization issue by introducing the L 1 norm to represent the distribution of the targets under the regularization framework. Third, according to the principle of majorization-minimization (MM) algorithm, a simple L 2 regularization issue was obtained to replace the difficult L 1 one, and the real target distribution was obtained by solving the L 2 regularization problem (We named it sparse with MM (SMM) superresolution algorithm for convenience). Then, in order to improve the computational efficiency of the algorithm, we adopted the second-order vector extrapolation idea to accelerate the conventional MM algorithm and solve the L 2 regularization problem. The simulation and real data verified that the proposed SFMM algorithm not only improves the azimuth resolution in radar forward-looking imaging but also increases convergence speed on the basis of SMM superresolution algorithm.
Highlights
Radar has been widely used in many military and civilian fields for its all-day and all-weather imaging ability [1], [2]
It has been confirmed that the azimuth signal of real aperture radar forward-looking imaging can be modeled as a convolution of targets distribution and antenna pattern, so the azimuth resolution can be improved by deconvolution methods [12], [13]
As for the sparse with fast majorization-minimization (SFMM) algorithms, despite some performance degradation after accelerating, the RT was much less than SFMM algorithm, and the degradation was inappreciable compared with truncated singular value decomposition (TSVD) and iterative adaptive approach (IAA) methods
Summary
Radar has been widely used in many military and civilian fields for its all-day and all-weather imaging ability [1], [2]. It has been confirmed that the azimuth signal of real aperture radar forward-looking imaging can be modeled as a convolution of targets distribution and antenna pattern, so the azimuth resolution can be improved by deconvolution methods [12], [13]. The sparse regularization is a widely used method to relax ill-posedness and achieve superresolution [20], [21] It improves the resolution by introducing targets prior information under regularization framework [22], [23]. The sparse regularization method is suitable to improve the azimuth resolution for radar forward-looking imaging. A sparse with fast majorization-minimization (SFMM) superresolution algorithm was proposed to improve the azimuth resolution of sparse targets in radar forward-looking imaging.
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