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A duct mapping method using least squares support vector machines
Author(s) -
Douvenot Rémi,
Fabbro Vincent,
Gerstoft Peter,
Bourlier Christophe,
Saillard Joseph
Publication year - 2008
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2008rs003842
Subject(s) - computer science , clutter , algorithm , radar , inversion (geology) , genetic algorithm , support vector machine , artificial intelligence , latin hypercube sampling , least squares function approximation , data mining , machine learning , mathematics , monte carlo method , telecommunications , paleontology , statistics , structural basin , estimator , biology
This paper introduces a “refractivity from clutter” (RFC) approach with an inversion method based on a pregenerated database. The RFC method exploits the information contained in the radar sea clutter return to estimate the refractive index profile. Whereas initial efforts are based on algorithms giving a good accuracy involving high computational needs, the present method is based on a learning machine algorithm in order to obtain a real‐time system. This paper shows the feasibility of a RFC technique based on the least squares support vector machine inversion method by comparing it to a genetic algorithm on simulated and noise‐free data, at 1 and 5 GHz. These data are simulated in the presence of ideal trilinear surface‐based ducts. The learning machine is based on a pregenerated database computed using Latin hypercube sampling to improve the efficiency of the learning. The results show that little accuracy is lost compared to a genetic algorithm approach. The computational time of a genetic algorithm is very high, whereas the learning machine approach is real time. The advantage of a real‐time RFC system is that it could work on several azimuths in near real time.