在传统的逆合成孔径雷达(ISAR)成像中,目标的运动补偿通常需要先进行包络对齐实现一维距离像对准,再进行相位校正,这种补偿方法称为级联运动补偿。低信噪比下的级联运动补偿容易增加误差积累,最终导致图像模糊散焦,严重影响成像质量,针对该问题文中提出了一种基于粒子群优化(PSO)联合运动补偿的ISAR成像算法,该算法分解目标的运动模型,通过高阶次展开,设置参数向量,通过图像的锐化度作为代价函数,寻找向量参数的最优值进行目标的联合运动补偿,使得ISAR成像得到最佳效果。在最优参数求解过程中,本文采用PSO 算法,运算时实现更快收敛,该算法提高了模型的求解速度,得到聚焦效果更好的ISAR成像。通过仿真数据和外场实测数据验证了算法的有效性。

In traditional inverse synthetic aperture radar (ISAR) imaging, target motion compensation usually requires envelope alignment to achieve one-dimensional range profile alignment, followed by phase correction. This compensation method is called cascaded motion compensation. Cascade motion compensation under low signal-to-noise ratio can easily increase error accumulation, ultimately leading to image blurring and defocusing, seriously affecting imaging quality. To address this issue, this paper proposes an ISAR imaging algorithm based on particle swarm optimization (PSO) combined with motion compensation. This algorithm decomposes the motion model of the target, sets parameter vectors through high-order expansion, and uses image sharpness as the cost function to find the optimal value of vector parameters for joint motion compensation of the target, resulting in the best ISAR imaging effect. In the process of solving the optimal parameters, this article adopts the particle swarm optimization (PSO) algorithm, which achieves faster convergence during operation. This algorithm improves the solving speed of the model and obtains ISAR imaging with better focusing effect. This article verifies the effectiveness of the algorithm through simulation data and field measurement data.

TN957;V279