Joint Range Alignment and Autofocus Method Based on Combined Broyden–Fletcher–Goldfarb–Shanno Algorithm and Whale Optimization Algorithm

Correct and robust translational motion compensation is necessary but challenging for inverse synthetic aperture radar (ISAR) imaging and target recognition. On the one hand, parametric translational motion compensation methods are only effective for polynomial translational models, and on the other hand, the noise robustness of range alignment-autofocus methods is not satisfactory. Therefore, this work proposes a joint range alignment and autofocus method based on the Combined Broyden–Fletcher–Goldfarb–Shanno algorithm and whale optimization algorithm (BFGS-WOA). The method aims to achieve accurate and robust compensation of profile shift and the phase error simultaneously without relying on a specific translational model. Specifically, we use Laplacian entropy and squared envelope entropy to construct a dynamic objective function. It can improve the robustness of translational error estimation. The translational error of each pulse can be estimated by minimizing the objective function. We adopt BFGS to solve the optimization to ensure the global convergence. WOA is introduced to determine the optimal step size of the iteration. Finally, the estimated translational error is used to perform range alignment and autofocus simultaneously. Experimental results of measured datasets demonstrate that the proposed method outperforms existing methods and has strong robustness for low signal-to-noise ratio (SNR) and sparse aperture.