BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:College of Engineering,Thesis/Dissertations DESCRIPTION:Topic: Environmentally Aware Bayesian Active Sonar Abstract: Th is work introduces a Bayesian framework for active sonar detection in a re fractive ocean waveguide associated with horizontally stratified sound spe ed profile (ssp) and an attendant reverberation field. The detector is con strained by significant depth uncertainty of the scattering body of intere st. The proposed scheme effectively quantifies the discriminating informat ion between two composite hypotheses associated with these uncertainties. The framework rigorously accounts for environmental properties regarding t he refractive media, integrating either theoretical surface and volume rev erberation models or in situ observations. Uncertainty in the reverberatio n field and the target's depth are systematically and sensibly incorporate d through proper marginalization. We consider broadband acoustic array obs ervations over a vertical aperture. The covariance structure associated wi th the depth dependent target is constructed based on all available enviro nmental information. The resulting Bayes Factor Active Sonar (BFAS) yields a set of range/time dependent quadratic forms operating within beam-delay space, minimizing average risk by optimally balancing the target-waveguid e eigen modes against noise and reverberation subspaces. By doing so, BFAS combines multi-path arrivals and attenuates reverberation and noise. This substantially increases signal-to-reverberation-plus-noise ratios. By lev eraging the small set of depth-invariant modes and their associated covari ance structure we establish lower bounds on processor performance. These b ounds demonstrate that even under severe target depth uncertainty, BFAS mu lti-path combining outperforms a conventional single specular arrival dete ctor even when that specular detector is conditioned on perfect knowledge of the target's depth. Extensive performance validation across various mea sured refractive and shallow-water environments lends credence to the robu stness of the Bayesian framework for active sonar system design. The frame work provides a roadmap for sensible treatment of diverse other uncertaint ies such as through an orthogonal modal decomposition of ssp. [Funded by the Office of Naval Research] Advisor(s): Dr. Paul J. Gendron, Profess or, Dept. of Electrical & Computer Engineering, UMASS Dartmouth Committe e Members: Dr. David A. Brown, Professor, Dept. of Electrical & Computer E ngineering, UMASS Dartmouth;Dr. Ana Doblas, Assistant Professor, Dept. of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Dayalan P. Kasilin gam, Professor, Dept. of Electrical & Computer Engineering, UMASS Dartmout h NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further informati on, please contact Dr. Paul J. Gendron via email at pgendron@umassd.edu.\n Event page: https://www.umassd.edu/events/cms/11-20-26-phd-qualifier-exam- -by-kenneth-t-bowers.php X-ALT-DESC;FMTTYPE=text/html:

Topic: Environmentally Aware Ba yesian Active Sonar

Abstract: This work introduces a Bayesian fram ework for active sonar detection in a refractive ocean waveguide associate d with horizontally stratified sound speed profile (ssp) and an attendant reverberation field. The detector is constrained by significant depth unce rtainty of the scattering body of interest. The proposed scheme effectivel y quantifies the discriminating information between two composite hypothes es associated with these uncertainties. The framework rigorously accounts for environmental properties regarding the refractive media\, integrating either theoretical surface and volume reverberation models or in situ obse rvations. Uncertainty in the reverberation field and the target's depth ar e systematically and sensibly incorporated through proper marginalization. We consider broadband acoustic array observations over a vertical apertur e. The covariance structure associated with the depth dependent target is constructed based on all available environmental information. The resultin g Bayes Factor Active Sonar (BFAS) yields a set of range/time dependent qu adratic forms operating within beam-delay space\, minimizing average risk by optimally balancing the target-waveguide eigen modes against noise and reverberation subspaces. By doing so\, BFAS combines multi-path arrivals a nd attenuates reverberation and noise. This substantially increases signal -to-reverberation-plus-noise ratios. By leveraging the small set of depth- invariant modes and their associated covariance structure we establish low er bounds on processor performance. These bounds demonstrate that even und er severe target depth uncertainty\, BFAS multi-path combining outperforms a conventional single specular arrival detector even when that specular d etector is conditioned on perfect knowledge of the target's depth. Extensi ve performance validation across various measured refractive and shallow-w ater environments lends credence to the robustness of the Bayesian framewo rk for active sonar system design. The framework provides a roadmap for se nsible treatment of diverse other uncertainties such as through an orthogo nal modal decomposition of ssp. [Funded by the Office of Naval Resear ch]

Advisor(s): Dr. Paul J. Gendron\, Professor\, Dept. of Electri cal & Computer Engineering\, UMASS Dartmouth

Committee Members: Dr. David A. Brown\, Professor\, Dept. of Electrical & Computer Engineerin g\, UMASS Dartmouth\;Dr. Ana Doblas\, Assistant Professor\, Dept. of Elect rical & Computer Engineering\, UMASS Dartmouth\; Dr. Dayalan P. Kasilingam \, Professor\, Dept. of Electrical & Computer Engineering\, UMASS Dartmout h

NOTE: All ECE Graduate Students are ENCOURAGED to attend. All in terested parties are invited to attend. Open to the public.

*For f urther information\, please contact Dr. Paul J. Gendron via email at pgend ron@umassd.edu.

Event page: https://www.umass d.edu/events/cms/11-20-26-phd-qualifier-exam--by-kenneth-t-bowers.php< /a>

DTSTAMP:20261112T150133 DTSTART;TZID=America/New_York:20261120T140000 DTEND;TZID=America/New_York:20261120T160000 LOCATION:Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A SUMMARY;LANGUAGE=en-us:ELEE Research Component of PhD Qualifier Exam By Ken neth T. Bowers UID:4403284ba4c26e7536fcec63136eb310@www.umassd.edu END:VEVENT END:VCALENDAR