Abstract
The distributed ISAR technique can form multiple virtual equivalent sensors to observe the target from multiple observation angles, and can obtain more spatial sampling data at the same time than monostatic ISAR, so it has the potential to increase the cross-range resolution. In this paper, sparse signal recovery algorithms are proposed to obtain the cross-range image of the distributed ISAR when the conventional Fourier transform imaging method is not applicable due to (1) nonuniform rotation of the target (2) large echo gap and (3) low echo SNR. After obtaining the distributed ISAR echoes, the range migration of the scatterer in each equivalent sensor is analyzed, and a cross-range phase that causes a positioning error is compensated. Then, the sparse representation of the echo in each range bin is given and the homotopy L1L0 (HL1L0) method is introduced. Singular value decomposition (SVD) is used to improve the robustness of the algorithm. Simulation results show that the sparse recovery algorithms can achieve high cross-range resolution, and HL1L0 method is better than orthogonal matching pursuit (OMP) and smoothed L0 (SL0) under different echo gaps and SNRs according to the four proposed evaluation criteria. Real data experiment verifies the advantage of the distributed ISAR and the effectiveness of the proposed method.
Published Version
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