NDLI: Multi-aspect discrimination of underwater mine-like object objects using hidden Markov models

Content Provider	IEEE Xplore Digital Library
Author	Salazar, J. Robinson, M. Azimi-Sadjadi, M.R.
Copyright Year	2002
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA (Salazar, J.; Robinson, M.; Azimi-Sadjadi, M.R.)
Abstract	The problem of classification of underwater targets involves discrimination between mine-like and non-mine-like objects as well as the characterization of background clutter. To improve performance of a given classifier, usually multiple aspects will be fused together in some fashion. In this work, a Hidden Markov Model (HMM) is used to make the overall decision. The HMM is a very powerful tool for using multiple observations to make a decision, as no decision is made until all the evidence is presented. In the past several years, much attention has been given in the area of automatic speech recognition to using multilayer perceptron (MLP) networks for estimating certain probabilities in the HMM framework. Several approaches are taken to this MLP/HMM idea in this paper and the results are compared. The test results presented are obtained on a wideband acoustic backscattered data set collected using four different objects with 1 degree of aspect separation for two different bottom (smooth and rough) conditions.
Starting Page	46
Ending Page	53
File Size	671531
Page Count	8
File Format	PDF
ISBN	0780375343
DOI	10.1109/OCEANS.2002.1193246
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-10-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Automatic speech recognition Acoustic testing Wideband Acoustic signal detection Object detection Acoustic signal processing Reverberation Neural networks Speech recognition
Content Type	Text
Resource Type	Article

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