Multi-platform Synthetic Aperture Imaging in Complex Environments via Microlocal Techniques

通过微局域技术在复杂环境中进行多平台合成孔径成像

• 批准号: 0830672
• 负责人: Birsen Yazici
• 金额: $ 52.71万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830672&HistoricalAwards=false
• 关键词: Multi; platform; Synthetic; Aperture; Imaging

Multi-platform Synthetic Aperture Imaging in Complex Environments via Microlocal TechniquesMultiple advances in diverse fields are expected to transition synthetic-aperture imaging technology from a dedicated single platform requiring an operator to a large number of small platforms operating autonomously. Such a swarm of sensors is expected to provide orders-of-magnitude performance gains relative to a single, dedicated platform. They are also expected to operate in complex environments involving dynamically changing scenes and multiple scattering. Such multi-platform synthetic-aperture imaging systems pose a number of challenges to image formation in addition to those involving wave propagation in complex environments. First, the requirement for scalability implies that the computational resources at each platform are limited and that moreover there may not be perfect phase coherency between platforms. This means the reconstruction algorithms have to be fast, decentralized and be able to handle phase errors. Second, the autonomy of the platforms implies non-ideal conditions from the perspective of image reconstruction: the sensors may be traversing arbitrary trajectories, and transmitting varying waveforms, etc. These challenges rule out the use of standard tomographic methods.This research involves developing theoretical foundations and corresponding constructive algorithms to address the challenges of multi-platform synthetic-aperture imaging. The fundamental developments of this project are applicable to all scattered-field-based synthetic-aperture imaging modalities, including RF and acoustics. Central to the project is microlocal analysis. This theory leads to powerful Generalized Filtered-BackProjection (GFBP) techniques that can accommodate non-ideal imaging conditions and wave propagation models for complex environments. This project investigates innovative extensions to microlocal techniques and integrates them with inverse scattering theory and statistical estimation and detection theory. Specifically, the investigators study GFBP algorithms for imaging in dynamically changing and multiple-scattering environments; analytic autofocus methods and fast GFBP algorithms.

通过微局部技术实现复杂环境下的多平台合成孔径成像不同领域的多项进展预计将使合成孔径成像技术从需要操作员的专用单一平台过渡到大量自主操作的小型平台。相对于单个专用平台，这样的传感器群有望提供数量级的性能增益。它们还有望在涉及动态变化场景和多重散射的复杂环境中工作。这种多平台合成孔径成像系统除了涉及复杂环境中的波传播之外，还对图像形成提出了许多挑战。首先，可扩展性的要求意味着每个平台的计算资源是有限的，而且平台之间可能没有完美的相位一致性。这意味着重建算法必须快速，分散，并能够处理相位误差。第二，平台的自主性意味着从图像重建的角度来看，非理想的条件：传感器可能会穿越任意的轨迹，并发送不同的波形，等等。这些挑战排除了使用标准的层析方法。本研究涉及发展的理论基础和相应的建设性算法，以解决多平台合成孔径成像的挑战。该项目的基本发展适用于所有基于散射场的合成孔径成像模式，包括RF和声学。该项目的核心是微观分析。这一理论导致强大的广义滤波反投影（GFBP）技术，可以适应非理想的成像条件和波传播模型的复杂环境。本项目研究微局部技术的创新扩展，并将其与逆散射理论和统计估计和检测理论相结合。具体来说，研究人员研究GFBP算法在动态变化和多重散射环境中的成像;分析自动聚焦方法和快速GFBP算法。