CAREER: Bayesian Graph Signal Processing for Machine Perception

University Of California-San Diego, La Jolla CA

Investigators

Abstract

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.