Three-dimensional SAR imaging with sparse linear array using tensor completion in embedded space

Abstract Due to the huge data storage and transmission pressure, sparse collection strategy has provided opportunities challenges for 3D SAR imaging. However, brought by linear array will produce high-level side-lobes, as well aliasing false-alarm targets. Simultaneously, vectorizing or matrixing of makes high computational complexity memory usage, which is not practicable in real applications. To deal with these problems, tensor completion (TC), a convex optimization problem, used solve imaging problem efficiently. Unfortunately, traditional TC methods are invalid incomplete missing slices arrays. In this paper, novel algorithm using embedded space proposed images efficient side-lobes suppression. With help sparsity low-rank property hidden radar signal, taken input converted into higher order Hankel multiway delay embedding transform (MDT). Then, tucker decomposition incremental rank been applied completion. Subsequently, any can be employed obtain excellent performance completed tensor. The method achieves resolution low-level compared TC-based methods. It verified several numerical simulations multiple comparative studies on data. Results clearly demonstrate that generate small reconstruction error even when sampling rate signal noise ratio low, confirms validity advantage method.