Rémi Douvenot

A duct mapping method using least squares support vector machines

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.

Retrieve the evaporation duct height by least-squares support vector machine algorithm

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.

Refractivity from sea clutter applied on VAMPIRA and Wallops’ 98 data

For coastal and ship-borne survey radars, coverage prediction is a critical question. In sea environment, the spatial variations in temperature conditions involve refractive index gradients and create atmospheric ducts. These events modify the radar coverage in the lower troposphere. A means to retrieve the refractive index gradients in the low troposphere is to use the Refractivity From Clutter (RFC). The principle of RFC is to compute the propagation factor from the radar received power, thus to process it in order to retrieve the refractive index profiles. In this paper, the RFC is applied on data obtained from VAMPIRA and Wallopspsila98 measurement campaigns. Since refractive index quickly varies, fast inversion methods based on a pre-generated database of propagation factors are used.

Inverse methods for Refractivity From Clutter

Predicting the radar coverage in low troposphere is a critical problem for detection of the low above sea flying or floating targets. To predict the radar coverage, the electromagnetic wave propagation can be modeled using parabolic wave equation resolution methods, but a description of the refractive index of the medium is required. Refractivity from clutter is a method to obtain this refractive index from the backscattered power received by the shipborne radar. An advantage of the method is that it does not require additional equipment. This paper presents a comparison of four inversion methods, whose two recent, in the refractivity from clutter purpose on ideal simulated data and shows the feasibility of a system using inversion methods based on pre-generated databases.