Multiple input multiple output radar imaging based on multidimensional linear equations and sparse signal recovery

Multiple input multiple output (MIMO) radar forms large virtual aperture and improves the cross-range resolution of radar imaging. Sparse signal recovery algorithms can be used to improve image quality of target with sparse property in spatial domain. Conventional sparse signal recovery-based MIMO radar imaging method rearranges the received two-dimensional (2D) or 3D signals into a vector, then linear equations describing the relation between the received signal and the reflectivity of the scatterers are solved. However, this method occupies huge memory spaces and increases the computational load. In this study, by introducing synthetic codes, multidimensional linear equations of MIMO radar imaging are derived, which occupy less memory spaces and cost less computationally. A L-1 L-0 norms homotopy sparse signal recovery algorithm for multidimensional linear equations is used to recover the image. Simulation results verify the high efficiency of using multidimensional linear equations.