University Events

Description: Topic: An Environmentally Informed Bayes Factor Approach for High Frequency Broadband Active Detection in Refractive Ocean Waveguides

Location: Science & Engineering Building (SENG), Room 212

Abstract:
Underwater active sonar detection systems face challenges in ocean environments due to ambient noise power levels, reverberation inherent with rough and moving boundaries and the volume scattering associated with the inhomogeneous media. In addition, ocean waveguides spread acoustic energy in angle and delay leading to images of the target body appearing at the receiver array via the set of refracted eigen rays of the waveguide. Such multipath angle and delay spread can confound conventional detection approaches. In this thesis, an axiomatic probabilistic approach, in conjunction with a ray theoretic modeling of the refractive waveguide is employed to construct an environmentally informed Bayes factor (BF) detector for monostatic active sonar operating in ocean waveguides. A Laplace approximation is employed to marginalize the composite alternative's target body depth uncertainty. Likewise, a multivariate Gaussian model suitable for high frequency reverberation allows for closed form marginalization under the composite null. It is found that the BF statistic takes the form of a time varying quadratic form that incorporates relevant waveguide information about the ocean environment as well as constraints on the target body depth. The structure of this quadratic form is illuminated and discussed and shown to be a time varying covariance test associated with the target body's angle delay response at the receiver array as a function of range. Case studies are employed on isovelocity, Pekeris, Munk, and constant gradient ocean profiles to develop and compare the time varying angle delay structure and reveal important features of the waveguide that provide the most information regarding the scattering body's presence. The key result is the BF extraction of depth invariant modes that provide significant leverage for detection.

Advisor(s): Dr. Paul J. Gendron, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth
Committee Members: Dr. David A. Brown, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Dayalan P. Kasilingam, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth

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 at 508.999.8510 or via email at pgendron@umassd.edu