从一个和稀疏观测方向测量的多频数据成像移动点源(第一部分):远场情况

IF 2.1 3区 数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SIAM Journal on Imaging Sciences Pub Date : 2023-08-17 DOI:10.1137/23m1545045
Hongxia Guo, Guanghui Hu, Guanqiu Ma
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引用次数: 0

摘要

我们提出了一种多频算法,用于从频域的一个和稀疏的远场观测方向中恢复运动点源的部分轨迹信息。假定运动源的起始和结束时间点都是已知的。我们引入了远场区域的可观测方向(角)的概念,并推导了直线运动和圆周运动的所有可观测方向(角)。不可观测方向的存在使得本文与逆平稳源问题有很大的不同。验证了在观测方向上,当观测方向与运动源速度矢量夹角为时,可成像包含轨迹且垂直于该方向的最小条带。否则,对于直线和圆周运动,人们只能期望恢复比这个最小条更薄的条。在稀疏观测方向上测量的远场数据可以用来恢复轨迹的-凸域。通过二维和三维数值算例验证了该方法的有效性和可行性。
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Imaging a Moving Point Source from Multifrequency Data Measured at One and Sparse Observation Directions (Part I): Far-Field Case
We propose a multifrequency algorithm for recovering partial information on the trajectory of a moving point source from one and sparse far-field observation directions in the frequency domain. The starting and terminal time points of the moving source are both supposed to be known. We introduce the concept of observable directions (angles) in the far-field region and derive all observable directions (angles) for straight and circular motions. The existence of nonobservable directions makes this paper much different from inverse stationary source problems. At an observable direction, it is verified that the smallest strip containing the trajectory and perpendicular to the direction can be imaged, provided the angle between the observation direction and the velocity vector of the moving source lies in . If otherwise, one can only expect to recover a strip thinner than this smallest strip for straight and circular motions. The far-field data measured at sparse observable directions can be used to recover the -convex domain of the trajectory. Both two- and three-dimensional numerical examples are implemented to show effectiveness and feasibility of the approach.
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来源期刊
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
3.80
自引率
4.80%
发文量
58
审稿时长
>12 weeks
期刊介绍: SIAM Journal on Imaging Sciences (SIIMS) covers all areas of imaging sciences, broadly interpreted. It includes image formation, image processing, image analysis, image interpretation and understanding, imaging-related machine learning, and inverse problems in imaging; leading to applications to diverse areas in science, medicine, engineering, and other fields. The journal’s scope is meant to be broad enough to include areas now organized under the terms image processing, image analysis, computer graphics, computer vision, visual machine learning, and visualization. Formal approaches, at the level of mathematics and/or computations, as well as state-of-the-art practical results, are expected from manuscripts published in SIIMS. SIIMS is mathematically and computationally based, and offers a unique forum to highlight the commonality of methodology, models, and algorithms among diverse application areas of imaging sciences. SIIMS provides a broad authoritative source for fundamental results in imaging sciences, with a unique combination of mathematics and applications. SIIMS covers a broad range of areas, including but not limited to image formation, image processing, image analysis, computer graphics, computer vision, visualization, image understanding, pattern analysis, machine intelligence, remote sensing, geoscience, signal processing, medical and biomedical imaging, and seismic imaging. The fundamental mathematical theories addressing imaging problems covered by SIIMS include, but are not limited to, harmonic analysis, partial differential equations, differential geometry, numerical analysis, information theory, learning, optimization, statistics, and probability. Research papers that innovate both in the fundamentals and in the applications are especially welcome. SIIMS focuses on conceptually new ideas, methods, and fundamentals as applied to all aspects of imaging sciences.
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