Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Channel Characterization and Object Classification in Non-Stationary and Uncertain Environments

Thiruneermalai Gomatam, Vikram (2016) Channel Characterization and Object Classification in Non-Stationary and Uncertain Environments. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

PDF (Dissertation Manuscript)
Primary Text

Download (5MB)


Classification of SONAR targets in underwater environments has long been a challenging problem. These are mainly due to the presence of undesirable effects like dispersion, attenuation and self-noise. Furthermore, we also have to contend with range dependent environments, like the continental shelf/littoral regions, where most of the human and aquatic life's activities occur.

Our work consists of analyzing the propagation in these environments from a pulse-evolution perspective. We look at cases where characterizing wave propagation using conventional Fourier-spectral analysis is infeasible for practical applications and instead resort to a phase-space approximation for it. We derive the phase-space approximations for a variety of propagating waves and limiting boundary conditions.

We continue our past work on invariant features to enhance classification performance; we simulate the derived features for waves with cylindrical spreading. Another area of our work includes looking at the equation governing the wave propagation from a phase space perspective. It has been shown before that reformulating the classical wave equation in the phase-space provides interesting insights to the solution of the equation. It has been posited that this would be especially useful for non-stationary functions, like the ones governing SONAR propagation underwater.

We perform classification of real world SONAR data measured by the JRP ( DRDC-Atlantic, NURC, ARL-PSU, NRL) program. We use a 'classic' MPE classifier on the given non-stationary and contrast its performance with an MPE classifier augmented by a Linear Time Varying (LTV) filter, to assess the impact of adding a time-varying pre-filter to a classifier (MPE) deemed optimal for stationary additive white Gaussian noise. We show that the addition of the time-varying pre-filter to augment the standard MPE classifier does increase the performance of the classifier.

Finally, we look at the self-noise problem that is commonly present in the littoral regions of the ocean, which also happens to be the region where most of shallow water sound propagation occurs. We look at phase-space approach to the stochastic models that simulate the effect of signal dependent noise reverberations and attempt to design time-varying estimators that would mitigate the problem at hand. We perform simulations that corroborate our premise. Further directions in the aforementioned areas are also presented.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Thiruneermalai Gomatam, Vikramvit8@pitt.eduVIT8
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorLoughlin, Patrickloughlin@pitt.eduLOUGHLIN
Committee MemberEl-Jaroudi, Amroamro@pitt.eduAMRO
Committee MemberZhi-Hong, Maomaozh@engr.pitt.eduZHM4
Committee MemberSejdić, Ervinesejdic@pitt.eduESEJDIC
Committee MemberCohen,
Date: 25 January 2016
Date Type: Publication
Defense Date: 9 January 2015
Approval Date: 25 January 2016
Submission Date: 19 November 2015
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 111
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Acoustics, Classification, Underwater, Sonar, time-frequency, detection, Kernels
Date Deposited: 25 Jan 2016 21:21
Last Modified: 15 Nov 2016 14:30


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item