Three-dimensional inverse synthetic aperture radar imaging based on compressive sensing

Inverse synthetic aperture radar (ISAR) can form two-dimensional (2D) electromagnetic images of a target, but it cannot provide the third dimensional information about the target. Conventional 3D turntable ISAR imaging requires data collection over densely azimuth-elevation samples, which needs a large amount of data storage. In this study, an effective 3D ISAR imaging algorithm for turntable model based on compressive sensing is proposed, which exploits the sparsity in the image domain to achieve 3D reconstruction by using a limited number of measurements. Firstly, the 3D data tensor is converted into a 2D matrix by stacking slices of data along one specific dimension; then a 2D optimisation reconstruction approach is applied to solve a sparsity-driven optimisation problem to obtain the 2D distribution of the scatterers. Lastly, 3D ISAR images are generated by rearranging the scatterer distribution in the 2D map into a 3D volume. This imaging scheme only needs a small number of measurements, and reduces the required memory and computational burden significantly. Simulation results are finally shown to validate the proposed algorithm.