Translational Motion Compensation Method Based On Global Butterfly Optimization Algorithm for ISAR Imaging

The complex motion of ISAR maneuvering targets can be fitted by high-order polynomials. Under the condition of a low signal-to-noise ratio, the existing ISAR translational compensation methods are difficult to evaluate the motion error efficiently and accurately. In response to these problems, this paper proposes an ISAR translational compensation method based on the Global Butterfly Optimization Algorithm (GBOA). First, the target translational component is modeled as a high-order polynomial form and the objective function is constructed with image entropy as the evaluation criterion. Then, the initial value of the optimization iteration can be achieved by rough estimation of the translational component. Finally, the Global Butterfly Optimization Algorithm is used to estimate and compensate for the translational error efficiently and accurately. In addition, based on the Butterfly Optimization Algorithm, the proposed method introduces a global variable factor, which makes GBOA has a higher convergence speed and the ability to avoid falling into a locally optimal solution. The experimental results of Yak-42 measured data verify the effectiveness and robustness of the proposed method.