Noise-Robust Motion Compensation for Aerial Maneuvering Target ISAR Imaging by Parametric Minimum Entropy Optimization

When a target is involved in maneuvering motion, the nonuniform 3-D rotation motion will cause a continuous change of image projection plane (IPP), which would induce 2-D spatial-variant phase errors. In this case, the inverse synthetic aperture (ISAR) image would be seriously blurred when using the traditional compensation methods. On the other hand, strong noise has been always challenging the conventional methods in motion parameters estimation and phase error compensation. In this paper, we propose a noise-robust compensation method to compensate the 2-D spatial-variant phase errors of the maneuvering target via using tracking information and parametric minimum entropy optimization. First, the maneuvering signal model is developed based on a 2-D spatial-variant model and a 3-D rotation motion model. Based on the developed signal model, a parametric entropy minimum optimization is established to estimate the rotation motion parameters. A gradient-based solver of this optimization is then adopted to iteratively find the global optimum. Meanwhile, in order to increase the robustness of this optimization under low SNR, an extended Kalman filter is adopted here for coarse motion estimation via using tracking information. By treating these estimated motion parameters as initial values, we can effectively prevent this optimization from trapping into a local optimum. Finally, the 2-D spatial-variant phase error can be iteratively compensated, and a well-focused ISAR image can be obtained. The proposed method has three main contributions: 1) it is applicable in the case of changing IPP; 2) it gives the exact expression of chip parameters; and 3) it can efficiently compensate the 2-D spatial-variant phase errors under low SNR. Experiments based on the simulated data and the real measured data prove the effectiveness and robustness of the proposed method.