Compressed Sensing ISAR 3D Imaging Methods Based on OMP Algorithm

In the application of three-dimensional imaging inverse synthetic aperture radar, the existing matching tracking-based compression tracking reconstruction algorithm has many problems, such as large computer storage and low computational efficiency. Based on the Orthogonal Matching Pursuit (OMP) algorithm, this paper proposes a dimensionality-compressive sensing reconstruction method Kron OMP based on the sparseness of the Inverse-Synthetic-Aperture-Radar (ISAR) target and the three-dimensional separability of the perceptual matrix. Firstly, a three-dimensional ISAR imaging model is established, and the Kronecker product expression method is used to split the perceptual matrix and transform the 3D reconstruction into a two-dimensional reconstruction problem. Comparing the time and memory consumption of the algorithm, the Kron OMP algorithm reduces the computer memory requirements of the perceptual matrix by more than 95% during the reconstruction process and reduces the computation time by more than 90%. Simulation experiments verify the effectiveness of the algorithm.