基于MCMC采样的扩展目标单站成像
Monostatic imaging of an extended target with MCMC sampling
AI总结：
本文针对单站测量配置下的平面扩展目标成像问题，提出基于MCMC采样的贝叶斯形状恢复方法，通过形状导数分析选择形状基，并结合单站采样与圆盘散射场显式表达式优化初始参数，数值模拟验证了方法有效性。
摘要：
我们考虑在单站测量配置下，由单个移动换能器测量的远场数据对平面扩展目标进行成像，这种配置在实际应用中经常遇到。本文提出了一种贝叶斯方法，利用MCMC采样恢复扩展目标的形状，其中基于测量数据的形状导数分析提出了一种新的形状基选择方法。为了优化初始圆盘的中心和半径，我们使用单站采样方法确定中心，并利用圆盘的显式散射场表达式确定半径。数值模拟验证了所提方法的有效性。
英文摘要：
We consider the imaging of a planar extended target from far-field data under a monostatic measurement configuration, in which the data is measured by a single moving transducer, as frequently encountered in practical application. In this paper, we develop a Bayesian approach to recover the shape of the extended target with MCMC sampling, where a new shape basis selection is proposed based on the shape derivative analysis for the measurement data. In order to optimize the center and radius of the initial disk, we use the monostatic sampling method for the center and the explicit scattered field expression for disks for the radius. Numerical simulations are presented to validate the proposed method.
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