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

Abstract

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

Three-dimensional reconstruction for space targets with multistatic inverse synthetic aperture radar systems

“…Obviously, ISAR images formed by range Doppler method can offer the range r k,n and the Doppler frequency f k,n . Therefore, the foundation of projection from the target 3D geometry to ISAR image is provided without cross-range scaling constraint in (3) and (4).…”

“…Previous attempts to form a 3D ISAR image can be roughly categorized into three types. The first class of methods to form a 3D image is a direct extension of the 2D ISAR concept [2,3]. In this method, a transmitter illuminates the target through a 2D angular aperture in both azimuth and elevation while simultaneously emitting wideband waveforms, 3D Fourier Transform (3D-FT) is applied to obtain a 3D ISAR image.…”

Compressive sensing‐based inverse synthetic radar imaging imaging from incomplete data

“…Specifically, the applicability of CS to the radar imaging problem has been justified by observing that at high frequencies a radar signal is sparse in the image domain, that is, when represented in the two‐dimensional (2D) Fourier basis, since it can be approximated by the superimposition of few prominent scatterer responses in the radar image plane with respect to the pixels in the image. CS reconstruction capabilities have been tested for a number of radar applications such as synthetic aperture radar (SAR) imaging and ISAR [7], ground penetrating radars [8], Multiple‐Input‐Multiple‐Output radar [9, 10] and 3D ISAR imaging [11]. Specifically, three different applications of CS to the ISAR imaging problem have been described in the literature.…”

Inverse synthetic aperture radar imaging of maneuvering target based on cubic chirps model with time-varying amplitudes

“…[1][2][3][4][5] The primary step for ISAR imaging is motion compensation, and this can be well implemented by the traditional methods. [1][2][3][4][5] The primary step for ISAR imaging is motion compensation, and this can be well implemented by the traditional methods.…”