NDLI: Multi-aspect acoustic classification of buried objects

Content Provider	IEEE Xplore Digital Library
Author	Robinson, M. Azimi-Sadjadi, M.R. Sternlicht, D.D. Lemonds, D.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA (Robinson, M.; Azimi-Sadjadi, M.R.)
Abstract	In 2001, broadband echoes from objects buried 1-3 feet in a sand bottom were collected with the prototype of a Buried Object Scanning Sonar (BOSS), currently being developed by Florida Atlantic University and EdgeTech Inc. The object field consisted of one mine-like object as well as numerous non-targets, including scuba tanks, a concrete block, an aluminum sphere and an ordnance shell. The BOSS system was suspended in the water from a cable and translated across the object field. The task of detecting and classifying target objects is very challenging using only single-aspect acoustic returns. Features were extracted from these acoustic returns and then applied to various back-propagation neural network (BPNN) classifiers. A non-linear decision-level fusion was then applied to perform multi-aspect classification. The results are presented in terms of the receiver operating characteristics (ROC) curves and confusion matrices for the data collected by the BOSS system.
Sponsorship	Dept. of the Navy Sci. and Technol. Coalition for Enhanced Marine Resources Orincon Ceros Harbour Offshore Inc
Starting Page	478
Ending Page	484
File Size	513124
Page Count	7
File Format	PDF
ISBN	0933957300
DOI	10.1109/OCEANS.2003.178627
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	MTS
Subject Keyword	Buried object detection Prototypes Sonar Concrete Aluminum Acoustic signal detection Object detection Feature extraction Data mining Neural networks
Content Type	Text
Resource Type	Article

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