论文标题
用于部分数据逆弹性问题的扩展采样 - 集结Kalman滤波器方法
An extended sampling-ensemble Kalman filter approach for partial data inverse elastic problems
论文作者
论文摘要
通常，当只有部分数据可用时，逆问题更具挑战性。在本文中，我们提出了一种两步方法，结合了扩展采样方法和集合卡尔曼滤波器，以使用部分数据重建弹性的刚性障碍物。在第一步中，未知障碍物的大致位置是通过扩展采样方法获得的。在第二步中，使用集合卡尔曼滤波器来重建形状。在第一步中获得的位置指导集合卡尔曼滤波器的初始粒子的构建，这对于第二步的性能至关重要。这两个步骤均基于相同的物理模型，并使用相同的散射数据。显示数值示例以说明所提出方法的有效性。
Inverse problems are more challenging when only partial data are available in general. In this paper, we propose a two-step approach combining the extended sampling method and the ensemble Kalman filter to reconstruct an elastic rigid obstacle using partial data. In the first step, the approximate location of the unknown obstacle is obtained by the extended sampling method. In the second step, the ensemble Kalman filter is employed to reconstruct the shape. The location obtained in the first step guides the construction of the initial particles of the ensemble Kalman filter, which is critical to the performance of the second step. Both steps are based on the same physical model and use the same scattering data. Numerical examples are shown to illustrate the effectiveness of the proposed method.
