A Three-Dimensional Imaging Algorithm of Downward-looking Sparse Linear Array SAR Based on Low-rank Tensor

In order to solving the problems of the inner structure damage and the high computation load brought by the vectorizing or matrixing of 3-D sparse data, the 3-D signal model is established in tensor space for downward-looking sparse linear array three-dimensional SAR. Based on this signal model, a three-dimensional SAR sparse imaging algorithm is proposed in this paper. The missing data firstly can be recovered by tensor completion on the assumption that the echo tensor is essentially low rank. Then, the resulting 3-D images can be well focused by any Fourier transform-based 3-D imaging algorithms with the recovered full-sampled data tensor. The proposed algorithm achieves not only high resolution and low-level side-lobes but also the ideal computational cost and memory consumption, which verified by several numerical simulations and multiple comparative studies on real data.