DocumentCode :
866336
Title :
Detection of oceanic electric fields based on the generalised likelihood ratio test (GLRT)
Author :
Donati, R. ; Le Cadre, J.-P.
Author_Institution :
Non Acoust. Detection Dept., GESMA, Brest, France
Volume :
149
Issue :
5
fYear :
2002
fDate :
10/1/2002 12:00:00 AM
Firstpage :
221
Lastpage :
230
Abstract :
Galvanic corrosion phenomena between the hull and the propeller of a ship induce static electric fields in sea water. These signatures can be observed on a vectorial electrical sensor and the authors investigate the design of a detection/localisation method based on the generalised maximum likelihood ratio test (GLRT). Incorporating a spatio-temporal analysis of the signals in the physical model, it is possible to partially estimate the trajectory of the target and to perform a detection decision. The resulting system consists in the calculation of the projection of the observation on a set of parameterised signatures and in selecting the projection that has the largest energy. An original method is proposed in order to determine the convenient partitioning of the set of projection bases. Due to the characteristics of the signals, classical results concerning performance analysis are not convenient and a specific framework is developed in order to analytically determine the behaviour of the system. A comparison with Monte Carlo simulations tends to prove the validity of the theory and the efficiency of the processing.
Keywords :
Monte Carlo methods; electric fields; electric sensing devices; maximum likelihood detection; oceanographic techniques; ships; GLRT; Monte Carlo simulations; detection decision; detection/localisation method; generalised maximum likelihood ratio test; hull; oceanic electric fields detection; parameterised signatures; performance analysis; physical model; projection bases; propeller; sea water; ship; spatio-temporal analysis; target trajectory estimation; vectorial electrical sensor;
fLanguage :
English
Journal_Title :
Radar, Sonar and Navigation, IEE Proceedings -
Publisher :
iet
ISSN :
1350-2395
Type :
jour
DOI :
10.1049/ip-rsn:20020491
Filename :
1047695
Link To Document :
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