1 January 2009 Retrieve the evaporation duct height by least-squares support vector machine algorithm
Remi Douvenot, Vincent Fabbro, Christophe Bourlier, Joseph Saillard, Hans-Helmut Fuchs, Helmut W. Essen, Joerg Foerster
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Abstract
The detection and tracking of naval targets, including low Radar Cross Section (RCS) objects like inflatable boats or sea skimming missiles requires a thorough knowledge of the propagation properties of the maritime boundary layer. Models are in existence, which allow a prediction of the propagation factor using the parabolic equation algorithm. As a necessary input, the refractive index has to be known. This index, however, is strongly influenced by the actual atmospheric conditions, characterized mainly by temperature, humidity and air pressure. An approach is initiated to retrieve the vertical profile of the refractive index from the propagation factor measured on an onboard target. The method is based on the LS-SVM (Least-Squares Support Vector Machines) theory. The inversion method is here used to determine refractive index from data measured during the VAMPIRA campaign (Validation Measurement for Propagation in the Infrared and RAdar) conducted as a multinational approach over a transmission path across the Baltic Sea. As a propagation factor has been measured on two reference reflectors mounted onboard a naval vessel at different heights, the inversion method can be tested on both heights. The paper describes the experimental campaign and validates the LS-SVM inversion method for refractivity from propagation factor on simple measured data.
Remi Douvenot, Vincent Fabbro, Christophe Bourlier, Joseph Saillard, Hans-Helmut Fuchs, Helmut W. Essen, and Joerg Foerster "Retrieve the evaporation duct height by least-squares support vector machine algorithm," Journal of Applied Remote Sensing 3(1), 033503 (1 January 2009). https://doi.org/10.1117/1.3081546
Published: 1 January 2009
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CITATIONS
Cited by 13 scholarly publications.
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KEYWORDS
Atmospheric propagation

Databases

Refractive index

Radar

Data modeling

Atmospheric modeling

Humidity

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