CAREER: Bayesian Graph Signal Processing for Machine Perception

职业：用于机器感知的贝叶斯图信号处理

• 批准号: 2146261
• 负责人: Florian Meyer
• 金额: $ 56.97万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146261&HistoricalAwards=false
• 关键词: CAREER; Bayesian; Graph; Signal; Processing

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.

机器感知的数学方法可以检测、定位和跟踪环境中的对象。他们将在海洋科学、机器人技术、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。现有方法依赖于简化和预处理阶段，这会降低测量的数据速率，但也会丢弃相关信息，从而限制性能。特别是，如果物体彼此靠近或它们产生的测量值很弱，现有方法通常无法可靠地感知它们。该项目旨在建立新的感知方法，最佳利用所有可用信息，在具有挑战性的场景中提供前所未有的性能。关键的原则，将使大量的传感器与高数据速率的使用，是系统地利用图形结构的感知问题的数学公式。将利用矢量传感器和大型水听器阵列提供的水声数据对所开发的方法进行评估。该项目产生的创新将大大提高海洋感知系统的性能，同时也将在自动驾驶、医学成像和无线通信等各种进一步应用中带来切实的进步。跨学科教育和推广活动旨在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它们在海上的部署。研究成果将传播给科学界，并用于在世界范围内提供的教材以及教程和短期课程。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。将采用“拉伸”或“打开”图节点的原则来用低维操作替换高维操作。基于这一原则，迭代感知方法的收敛保证，以及大大降低计算复杂性和上级可扩展性将被开发。与传统的对象感知方法相反，所设想的方法的高度可扩展性使得可以生成和保持非常大量的对象假设，并且进而提高感知性能。特别感兴趣的是其中为每个真实的或复值数据单元（样本、像素或箱）形成新的对象假设的方法，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，该方法嵌入物理环境的模拟器，以利用多径传播和虚拟孔径，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鲁棒性。该项目还将设计基于图形的机器感知方法的扩展，通过从数据中学习的信息自适应地改进底层统计模型。这里，描述感知问题的原始统计模型的图由图神经网络支持，该图神经网络用标记的真实的数据或用由物理环境的模拟器提供的合成数据训练。最后，一个开放的软件和硬件平台，用于展示感知能力，将汇集研究和教育，并支持全面的推广和传播活动。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

Towards Terrain-Based Navigation Using Side-Scan Sonar

• DOI: 10.23919/fusion52260.2023.10224175
• 发表时间: 2023-06
• 期刊: 2023 26th International Conference on Information Fusion (FUSION)
• 作者: Ellen Davenport;Junsu Jang;Florian Meyer

Neural Enhanced Belief Propagation for Multiobject Tracking

• DOI: 10.1109/tsp.2023.3314275
• 发表时间: 2022-12
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Mingchao Liang;Florian Meyer

Passive Acoustic Tracking of Whales in 3-D

• DOI: 10.1109/icassp49357.2023.10096584
• 发表时间: 2023-06
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Junsu Jang;Florian Meyer;Eric R. Snyder;S. Wiggins;S. Baumann‐Pickering;J. Hildebrand

A BP Method for Track-Before-Detect

• DOI: 10.1109/lsp.2023.3296874
• 发表时间: 2023-07
• 期刊: IEEE Signal Processing Letters
• 影响因子: 3.9
• 作者: Mingchao Liang;Thomas Kropfreiter;Florian Meyer

Automating multi-target tracking of singing humpback whales recorded with vector sensors

自动对用矢量传感器记录的鸣叫座头鲸进行多目标跟踪

• DOI: 10.1121/10.0021972
• 发表时间: 2023
• 期刊: The Journal of the Acoustical Society of America
• 作者: Gruden, Pina;Jang, Junsu;Kügler, Anke;Kropfreiter, Thomas;Tenorio-Hallé, Ludovic;Lammers, Marc O.;Thode, Aaron;Meyer, Florian
• 通讯作者: Meyer, Florian