ISAR Imaging for Maneuvering Targets with Complex Motion Based on Generalized Radon-Fourier Transform and Gradient-Based Descent under Low SNR

Abstract

The existing inverse synthetic aperture radar (ISAR) imaging algorithms for ship targets with complex three-dimensional (3D) rotational motion are not applicable because of continuous change of image projection plane (IPP), especially under low signal-to-noise-ratio (SNR) condition. To overcome this obstacle, an efficient approach based on generalized Radon Fourier transform (GRFT) and gradient-based descent optimal is proposed in this paper. First, the geometry and signal model for nonstationary IPP of ship targets with complex 3-D rotational motion is established. Furthermore, the two-dimensional (2D) spatial-variant phase errors caused by complex 3-D rotational motion which can seriously blur the imaging performance are derived. Second, to improve the computational efficiency for 2-D spatial-variant phase errors compensation, the coarse motion parameters of ship targets are estimated via the GRFT method. In addition, using the gradient-based descent optimal method, the global optimum solution is iteratively estimated. Meanwhile, to solve the local extremum for cost surface obtained via conventional image entropy, the image entropy combined with subarray averaging is applied to accelerate the global optimal convergence. The main contributions of the proposed method are: (1) the geometry and signal model for ship targets with a complex 3-D rotational motion under nonstationary IPP are established; (2) the image entropy conjunct with subarray averaging operation is proposed to accelerate the global optimal convergence; (3) the proposed method can ensure the imaging accuracy even with high imaging efficiency thanks to the sole optimal solution generated by using the subarray averaging and image entropy. Several experiments using simulated and electromagnetic data are performed to validate the effectiveness of the proposed approach.