Please use this identifier to cite or link to this item:
Title: Optimal and near-optimal signal detection in snapping shrimp dominated ambient noise
Authors: Chitre, M.A. 
Potter, J.R. 
Ong, S.-H. 
Keywords: Detection
Impulse noise
Snapping shrimp noise
Issue Date: Apr-2006
Citation: Chitre, M.A., Potter, J.R., Ong, S.-H. (2006-04). Optimal and near-optimal signal detection in snapping shrimp dominated ambient noise. IEEE Journal of Oceanic Engineering 31 (2) : 497-503. ScholarBank@NUS Repository.
Abstract: The optimal detection of signals requires detailed knowledge of the noise statistics. In many applications, the assumption of Gaussian noise allows the use of the linear correlator (LC), which is known to be optimal in these circumstances. However, the performance of the LC is poor in warm shallow waters where snapping shrimp noise dominates in the range 2-300 kHz. Since snapping shrimp noise consists of a large number of individual transients, its statistics are highly non-Gaussian. We show that the noise statistics can be described accurately by the symmetric α-stable family of probability distributions. Maximum-likelihood (ML) and locally optimal detectors based on the detailed knowledge of the noise probability distribution are shown to demonstrate enhanced performance. We also establish that the sign correlator, which is a nonparametric detector, performs better than the LC in snapping shrimp noise. Although the performance of the sign correlator is slightly inferior to that of the ML detector, it is very simple to implement and does not require detailed knowledge of the noise statistics. This makes it an attractive compromise between the simple LC and the complex ML detector. © 2006 IEEE.
Source Title: IEEE Journal of Oceanic Engineering
ISSN: 03649059
DOI: 10.1109/JOE.2006.875272
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jan 19, 2023


checked on Jan 19, 2023

Page view(s)

checked on Jan 26, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.