Deep Learning for Passive RF Imaging

用于无源射频成像的深度学习近年来，深度学习在广泛的机器学习应用中取得了巨大的成功。然而，它作为成像数学工具的潜力还有待探索。该项目开发了基于深度学习的理论，方法和算法，用于在复杂环境中使用机会照明器进行无源射频（RF）成像。随着无线通信和广播信号的激增，无源RF成像已成为有源成像的潜在替代方案，具有若干优点。被动成像不需要频谱分配。它对环境友好，能够进行隐形操作。无源射频系统重量轻、体积小、价格便宜、易于建造和操作，因此适合部署在小型无人机（UAV）上。这些特性使被动合成孔径雷达（SAR）技术适用于从农业到基础设施监测的大量日常民用应用。虽然基于无人机的被动SAR有可能在许多应用领域带来革命性的成像，但部署这些系统的根本瓶颈之一是成像方面的挑战。与主动成像不同，被动SAR图像重建涉及许多未知数和不确定性，包括发射机位置、发射波形、多次散射和动态变化的波传播环境以及有限的通信和计算资源。这些挑战排除了现有方法的使用，例如通常的基于傅立叶变换或迭代的方法。该项目采用了一种完全不同的成像方法，并将基于物理的建模和图像重建解释为机器学习任务。深度学习擅长从数据中自动提取特征，绕过建模和特征工程的手工制作过程。成像的常规方法涉及基于物理和统计的建模、估计、断层扫描和优化。该项目的核心是消除不同专业领域之间的这种分离，学习和改进模型，并在深度学习框架内从训练数据中进行优化。这利用了深度学习生成复杂的非线性函数的能力，以联合学习波传播和先验模型和超参数，从而提高准确性和鲁棒性。网络设计的范围从完全数据驱动的无模型方法到由贝叶斯推理和优化理论指导的方法。由此产生的图像重建方法预计将更加强大和准确的不确定性和动态变化的环境和未知的成像参数和计算效率比国家的最先进的alternatives.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Deep Learning for Passive Radio Frequency ImagingDeep Learning has enjoyed a spectacular success in wide range of machine learning applications in recent years. However, its potential as a mathematical tool in imaging is yet to be explored. This project develops a Deep Learning based theory, methods and algorithms for passive Radio Frequency (RF) imaging in complex environments using illuminators of opportunity. With the proliferation of wireless communications and broadcasting signals, passive RF imaging has emerged as a potential alternative to active imaging with several advantages. Passive imaging does not require spectrum allocation. It is environmentally friendly and capable of stealth operations. Passive RF systems are lightweight, small, inexpensive, and easy to build and operate, making them suitable for deployment on small uninhabited aerial vehicles (UAVs). These attributes make passive synthetic aperture radar (SAR) technology suitable for a vast array of everyday civilian applications ranging from agriculture to infrastructure monitoring.While UAV-based passive SAR has the potential to revolutionize imaging across many domains of applications, one of the fundamental bottlenecks in the deployment of these systems is the challenges in image formation. Unlike active imaging, passive SAR image reconstruction involves many unknowns and uncertainties including transmitter locations, transmitted waveforms, multiply scattering and dynamically changing wave propagation environments and limited communication and computational resources. These challenges rule out the use of existing methods such as the usual Fourier transform based or iterative ones.This project takes a radically different approach to imaging and interprets physics-based modeling and image reconstruction as machine learning tasks. Deep Learning excels in extracting features from data automatically bypassing hand-crafting process of modeling and feature engineering. Conventional approach to imaging involves physics based and statistical modeling, estimation, tomography and optimization. Central to this project is to remove this separation between different domains of expertise and learn and refine models and perform optimization within Deep Learning framework from training data. This takes advantage of Deep Learning's ability to generate complex, non-linear functions to jointly learn wave propagation and prior models and hyperparameters to improve accuracy and robustness. The network designs range from entirely data-driven model-free approaches to ones that are guided by Bayesian inference and optimization theory. The resulting image reconstruction methods are expected to be more robust and accurate with respect to uncertain and dynamically changing environments and unknown imaging parameters and computationally more efficient than state-of-the-art alternatives.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.