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层析合成孔径雷达成像(TomoSAR)是通过同一观测区域不同入射角的多幅二维合成孔径雷达(SAR)图像在高程向进行孔径合成,从而实现三维成像。近年来,压缩感知(CS)被用于高程向稀疏场景的重建,高程向重建质量取决于观测矩阵的性质,而航迹分布是影响观测矩阵重构性能的重要因素。相比于度量观测矩阵重构性能的其他约束条件,RIPless理论具有有效、直观和计算简单等优点。提出了一种基于RIPless理论的压缩感知层析SAR成像航迹分布优化准则,从而在航迹数目一定的情况下,获取最优分布以实现高程向优化重建。最后,通过仿真和实验验证了所提优化准则的有效性。
The tomosynthesis aperture radar imaging (TomoSAR) is a three-dimensional imaging of multiple two-dimensional synthetic aperture radar (SAR) images with different angles of incidence in the same observation area. In recent years, compressed sensing (CS) has been used to reconstruct elevation to sparse scenes. The quality of elevation depends on the nature of the observation matrix. Track distribution is an important factor affecting the reconstruction performance of observation matrix. Compared with other constraints that measure the reconstruction performance of the observing matrix, RIPless theory is effective, intuitive and easy to calculate. This paper presents a new optimization criterion of compressed track tomography based on RIPless theory to get the optimal distribution to achieve the elevation reconstruction to the optimal reconstruction with a certain number of tracks. Finally, the effectiveness of the proposed optimization criterion is verified by simulation and experiments.