Fusion and Gaussian mixture based classifiers for SONAR data

Vikas Kotari; KC Chang

doi:10.1117/12.883697

5 May 2011 Fusion and Gaussian mixture based classifiers for SONAR data

Vikas Kotari, KC Chang

Proceedings Volume 8050, Signal Processing, Sensor Fusion, and Target Recognition XX; 80500U (2011) https://doi.org/10.1117/12.883697
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Underwater mines are inexpensive and highly effective weapons. They are difficult to detect and classify. Hence detection and classification of underwater mines is essential for the safety of naval vessels. This necessitates a formulation of highly efficient classifiers and detection techniques. Current techniques primarily focus on signals from one source. Data fusion is known to increase the accuracy of detection and classification. In this paper, we formulated a fusion-based classifier and a Gaussian mixture model (GMM) based classifier for classification of underwater mines. The emphasis has been on sound navigation and ranging (SONAR) signals due to their extensive use in current naval operations. The classifiers have been tested on real SONAR data obtained from University of California Irvine (UCI) repository. The performance of both GMM based classifier and fusion based classifier clearly demonstrate their superior classification accuracy over conventional single source cases and validate our approach.

Citation Download Citation

Vikas Kotari and KC Chang "Fusion and Gaussian mixture based classifiers for SONAR data", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500U (5 May 2011); https://doi.org/10.1117/12.883697

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available