ELEE Oral Comprehensive Exam for Doctoral Candidacy by Abner C. Barros - ECE
Topic: Computational Bayesian Inference for Localization in Active Sonar and Distributed Acoustic Sensing Systems
Abstract:
Spatiotemporal inference regarding objects from sensed acoustic and seismic fields is challenging due to the nature of the environment and the objects’ effect on propagating wavefields. Nevertheless, there are often diverse streams of useful and readily available prior information on both the media and the object’s composition and state of motion that should be brought to bear on such localization problems. This dissertation proposal seeks to advance the breadth of the Bayesian framework, focusing on computationally efficient methods for inference in active underwater acoustic systems and remote passive seismic sensing. Two inverse problems are of interest: localization of a mobile submerged object using high-frequency active sonar with a small-aperture array in refractive ocean waveguides, and terrestrial seismic spatial inference using a fiber–optic distributed acoustic sensing (DAS) system. In the active sonar case, the complex dependencies of acoustic propagation in refractive, multipath environments and a rich scattering body motivate the development of methods capable of resolving closely spaced arrivals. Computational Bayesian methods are proposed to allow resources to be allocated judiciously while preserving fidelity to prior information and acoustic observations. Joint posterior density functions of eigenrays’ wavevectors characterize the scattered field and are associated with the angle and Doppler spread arrivals in an uncertain refractive waveguide. A lower–dimensional subspace representation of sound speed uncertainty must be exploited, with preliminary results showing promise [Barros, Gendron JASA-EL 2025]. For DAS, challenges arise from its strain measurement principle and channel directivity, limiting the direct application of traditional array processing techniques. Uncertainty in seismic propagation speeds and propagation characteristics of surface waves further complicate inference. The proposed research aims to improve DAS localization performance beyond conventional and naive “proximity” detection through the development of a hierarchical Bayesian approach.
Advisor: Dr. Paul J. Gendron, Associate Professor, Department of Electrical & Computer Engineering, ½ûÂþÌìÌÃ
Committee members:
Dr. David A. Brown, Professor, Department of Electrical & Computer Engineering, ½ûÂþÌìÌÃ;
Dr. Dayalan P. Kasilingam, Professor & Chairperson, Department of Electrical & Computer Engineering, ½ûÂþÌìÌÃ;
Dr. Zoi-Heleni Michalopoulou, Professor, Mathematical Sciences, New Jersey Institute of Technology;
Dr. Tod Luginbuhl, Senior Research Scientist, Naval Undersea Warfare Center Division Newport
NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public.
*For further information, please contact Dr. Paul J. Gendron
Zoom Link:
Meeting ID: 99573148168
Passcode: 761573
Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
285 Old Westport Road, North Dartmouth, MA 02747
Paul J. Gendron
508.999.8540
pgendron@umassd.edu