Anthony Fiche

Analysis of X-Band SAR Sea-Clutter Distributions at Different Grazing Angles

Modeling sea clutter is a difficult problem due to the interaction of the sea surface characteristics (wind speed and wind direction), the geometry of acquisition (grazing angle and azimuth angle), and radar parameters (frequency, polarization, and resolution). In synthetic aperture radar (SAR) imagery, the effect of coherent averaging and motion from the sea will also influence the statistics. An accurate description of the sea surface amplitude probability density function is important for robust target detection. The goal of this paper is to identify a common distribution which matches two different airborne SAR X-band data sets obtained from different locales and at different grazing angles. The first data set was collected by the French Aerospace Laboratory (ONERA) SETHI radar off the coast of France, at low grazing angles (3° and 10°). The second is the Ingara medium grazing angle (15°-45°) sea clutter data set collected by the Defence Science and Technology Organisation off the coast of Australia. In this paper, a number of recently developed distributions are considered, including the K+Rayleigh and Pareto+noise, both of which account for thermal noise. To measure the effectiveness of the model fit, the Bhattacharyya distance and the threshold error are computed with particular attention given to the tail region, where the threshold is determined in a detection scenario.

Features modeling with an α-stable distribution: Application to pattern recognition based on continuous belief functions

The aim of this paper is to show the interest in fitting features with an @a-stable distribution to classify imperfect data. The supervised pattern recognition is thus based on the theory of continuous belief functions, which is a way to consider imprecision and uncertainty of data. The distributions of features are supposed to be unimodal and estimated by a single Gaussian and @a-stable model. Experimental results are first obtained from synthetic data by combining two features of one dimension and by considering a vector of two features. Mass functions are calculated from plausibility functions by using the generalized Bayes theorem. The same study is applied to the automatic classification of three types of sea floor (rock, silt and sand) with features acquired by a mono-beam echo-sounder. We evaluate the quality of the @a-stable model and the Gaussian model by analyzing qualitative results, using a Kolmogorov-Smirnov test (K-S test), and quantitative results with classification rates. The performances of the belief classifier are compared with a Bayesian approach.

Statistical analysis of low grazing angle high resolution X-band SAR sea clutter

This paper investigates the statistical analysis of two low grazing angle (3° and 10°) synthetic aperture radar datasets collected by ONERAs SETHI X-band radar off the coast of France. The focus of the work is to find the most suitable probability density function which matches the data. Particular attention is paid to the tail region, where the threshold is determined in a detection scenario. To measure the effectiveness of each model fit, the Bhattacharyya metric has been extended to measure the goodness of fit in this region.

Continuous belief functions and α-stable distributions

The theory of belief functions has been formalized in continuous domain for pattern recognition. Some applications use assumption of Gaussian models. However, this assumption is reductive. Indeed, some data are not symmetric and present property of heavy tails. It is possible to solve these problems by using a class of distributions called α-stable distributions. Consequently, we present in this paper a way to calculate pignistic probabilities with plausibility functions where the knowledge of the sources of information is represented by symmetric α-stable distributions. To validate our approach, we compare our results in special case of Gaussian distributions with existing methods. To illustrate our work, we generate arbitrary distributions which represents speed of planes and take decisions. A comparison with a Bayesian approach is made to show the interest of the theory of belief functions.

Characterization of em sea clutter with α-stable distribution

In this contribution, an accurate description of the ocean backscatter from a probability density function is proposed. The Elfouhaily spectrum has been used to generate a realistic sea surface. The scattering field will be computed by using the Physical Optics (PO). The K distribution has been already used to characterize the Radar Cross Section (RCS) of the sea surface. However, the probability density function of the RCS can have heavy tails. Consequently, we use the α-stable distributions which can take care the property of heavy tails. The probability density function is estimated with a least squared method. We finally compare the results obtained with each model by using the Kolmogorov-Smirnov test from several random surfaces and a statistical study is made by giving a boxplot of the estimated parameters of the α-stable distribution.

A Comparison between a Bayesian Approach and a Method Based on Continuous Belief Functions for Pattern Recognition

The theory of belief functions in discrete domain has been employed with success for pattern recognition. However, the Bayesian approach performs well provided that once the probability density functions are well estimated. Recently, the theory of belief functions has been more and more developed to the continuous case. In this paper, we compare results obtained by a Bayesian approach and a method based on continuous belief functions to characterize seabed sediments. The probability density functions of each feature of seabed sediments are unimodal and estimated from a Gaussian model and compared with an α-stable model.

Models of belief functions — Impacts for patterns recognitions

In a lot of operational situations, we have to deal with uncertain and inaccurate information. The theory of belief functions is a mathematical framework useful to handle this kind of imperfection. However, in most of the cases, uncertain data are modeled with a distribution of probability. We present in this paper different principles to induce belief functions from probabilities. Hence, we decide to use these functions in a pattern recognition problem. We discuss about the results we obtain according the way we generate the belief function. To illustrate our work, it will be applied to seabed characterization.

Digital compensation of lowpass filters imperfection in the Modulated Wideband Converter compressed sensing scheme for radio frequency monitoring

Abstract This paper focuses on non-ideal filters in a Modulated Wideband Converter (MWC) scheme. The MWC is a system that can sample a sparse wideband signal at sub-Nyquist rate. Generally, the output of the ideal MWC components will ensure a perfect reconstruction. In practice, the reconstruction should be based on the output of non-ideal components, especially filters. The impact of non-ideal filters will trigger to a bad reconstruction. In this paper, a detailed study on non-ideal lowpass filters imperfection used in compressed sensing MWC scheme is synthesized. A digital post-treatment scheme with amplitude and phase compensation is proposed after real lowpass filtering step in order to have the filtered output as close as the ideal lowpass filter output. At last, reconstruction spectra obtained from different simulated lowpass filters are compared with different parameters of MWC.

Combination of empirical/asymptotic models to characterize sea clutter at intermediate angles

In this paper, we propose to characterize normalized reflectivity of sea clutter at intermediate angles. Two approaches are generally used to model radar signal backscatter by the sea: empirical models based on measurements and asymptotic models based on physics. We choose to interpolate an unified model which combines the Georgia Institute of Technology model (GIT model), defined for low grazing angles, and the GOSSA model, developed by the French Mediterranean Institut of Oceanography, former Fresnel Institut, defined for high angles. The curve fitting of data is made by using the Lagrange and spline interpolations. The results are compared with a set of data (Nathanson and Masuko et al.) and with two mean backscatter models, one developed by the Defence Science and Technology Organisation (DSTO) called IRSG-LIN model and the IGENCE model.