Li Jishun, Zhang Yasheng, Yin Canbin, Xu Can, Li Pengju, He Jun
Graduate School, Space Engineering University, Beijing 101416, China.
Sensors (Basel). 2024 Jul 3;24(13):4332. doi: 10.3390/s24134332.
Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk-Nelder-Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.
空间目标在轨道上高速运动，因此为了获得高质量成像，需要进行高速运动补偿（HSMC）和平动补偿（TMC）。HSMC和TMC通常相邻，HSMC的残余误差会降低TMC的精度。同时，在低信噪比（SNR）条件下，HSMC和TMC的精度也会下降，这给高质量逆合成孔径雷达（ISAR）成像带来了挑战。因此，本文提出了一种基于低信噪比条件下熵最小化的联合ISAR运动补偿算法。首先，分析空间目标的运动，得到回波信号模型。然后，将空间目标的运动建模为高阶多项式，建立高速运动和平动的参数化联合补偿模型。最后，以联合运动补偿后的图像熵为目标函数，采用红尾鹰-单纯形（RTH-NM）算法估计目标运动参数，并进行联合补偿。仿真数据和实际数据的实验结果验证了所提算法的有效性和鲁棒性。
