CAREER: Bayesian Graph Signal Processing for Machine Perception

机器感知的算法方法可以检测、定位和跟踪环境中的对象。他们将在海洋科学、机器人、自动驾驶、室内定位和人群计数等领域建立新的服务和应用。现有的方法依赖于简化和预处理阶段，这降低了测量的数据速率，但也丢弃了相关信息，从而限制了性能。特别是，如果对象彼此靠近，或者它们产生的测量很弱，现有的方法往往无法可靠地感知它们。该项目旨在建立新的感知方法，以最大限度地利用所有可用信息，在具有挑战性的场景中提供前所未有的性能。能够以高数据速率使用大量传感器的关键原则是系统地利用感知问题的数学公式中的图结构。将使用矢量传感器和大型水听器阵列提供的水声数据对开发的方法进行评估。该项目产生的创新将大幅提高海洋感知系统的性能，并在包括自动驾驶、医学成像和无线通信在内的各种进一步应用方面取得切实进展。跨学科教育和外联活动的目的是让不同的学生群体接触最先进的机器学习和感知技术，以及他们在海上的部署。研究成果将被传播给科学界，并用于教材以及将在世界范围内展示的教程和短期课程。该项目将引入基于图形的估计，以建立利用所有可用信息的感知方法，从而产生前所未有的感知性能。采用“拉伸”或“打开”图结点的原理，用低维运算代替高维运算。基于这一原则，将开发出具有收敛保证、计算复杂度大大降低和良好可扩展性的迭代感知方法。与传统的对象感知方法相反，设想的方法的高可伸缩性使得有可能生成和维护非常大量的对象假设，并进而提高感知性能。特别感兴趣的是这样的方法，其中为每个实值或复值数据单元(样本、像素或库)形成新的对象假设，并且每个数据单元在基于整体图的框架中概率地与对象假设相关联。此外，研究小组将引入基于图形的估计方法，嵌入物理环境的模拟器，以利用多路径传播和虚拟光圈，目的是改善对低可观测对象的感知，并提供对不确定环境参数的稳健性。该项目还将设计基于图形的机器感知方法的扩展，根据从数据中学习的信息自适应地改进基本统计模型。这里，描述感知问题的原始统计模型的图由用标记的真实数据或用由物理环境的模拟器提供的合成数据训练的图神经网络支持。最后，一个展示感知能力的开放软件和硬件平台将把研究和教育结合在一起，并支持全面的外展和传播活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Algorithmic methods for machine perception can detect, localize, and track objects in the environment. They will establish new services and applications in fields such as ocean sciences, robotics, autonomous driving, indoor localization, and crowd counting. Existing methods rely on simplifications and preprocessing stages that reduce the data rate of the measurements, but also discard relevant information and thus limit performance. In particular, if objects are close to each other or the measurements they generate are weak, existing methods are often unable to perceive them reliably. This project aims to establish new perception methods that make optimal use of all the available information to provide unprecedented performance in challenging scenarios. The key principle that will enable the use of a large number of sensors with high data rates, is to systematically exploit graph structures in the mathematical formulation of perception problems. The developed methods will be evaluated using underwater acoustic data provided by vector sensors and large arrays of hydrophones. Innovation resulting from this project will substantially improve the performance of marine perception systems but also lead to tangible advances in a variety of further applications including autonomous driving, medical imaging, and wireless communication. Interdisciplinary education and outreach activities aim to expose a diverse cohort of students to state-of-the-art machine learning and perception techniques as well as their deployment at sea. Research results will be disseminated to the scientific community and used in teaching materials as well as tutorials and short courses to be presented worldwide.This project will introduce graph-based estimation to establish perception methods that make use of all the available information and thus yield unprecedented perception performance. The principle of "stretching" or "opening" graph nodes will be employed to replace high-dimensional operations by lower-dimensional ones. Based on this principle, iterative perception methods with convergence guarantees as well as strongly reduced computational complexity and superior scalability will be developed. Contrary to conventional object perception approaches, the high scalability of the envisaged methods makes it possible to generate and maintain a very large number of object hypotheses and, in turn, improve perception performance. Of particular interest are methods where a new object hypothesis is formed for each real- or complex-valued data cell (sample, pixel, or bin), and each data cell is probabilistically associated with an object hypothesis in a holistic graph-based framework. In addition, the research team will introduce graph-based estimation methods that embed simulators of the physical environment to exploit multipath propagation and virtual apertures with the goal of improving the perception of low-observable objects and providing robustness against uncertain environmental parameters. The project will also devise an extension of graph-based machine perception methods that adaptively refines the underlying statistical model by information learned from data. Here, the graph that describes the original statistical model of the perception problem is supported by a graph neural network trained with labeled real data or with synthesized data provided by simulators of the physical environment. Finally, an open software and hardware platform for the demonstration of perception capabilities will bring together research and education as well as support comprehensive outreach and dissemination activities.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.