Jean-Philippe Ovarlez

Adaptive MIMO radar detection in non-Gaussian and heterogeneous clutter considering fluctuating targets

Previously, the Generalized Likelihood Ratio Test - Linear Quadratic (GLRT-LQ) has been extended to the Multiple-Input Multiple-Output (MIMO) case where all transmit-receive subarrays are considered jointly as a system such that only one detection threshold is used. The new MIMO detector is Constant False Alarm Rate (CFAR) with respect to the clutter power fluctuations. In this paper, the adaptive version of this detector is considered, as well as a fluctuating target model similar to that of the Swerling Target. The degradation of the detection performance due to the estimation of the covariance matrix and the fluctuation of the target is studied through simulations for both the well-known Optimum Gaussian Detector (OGD) and the new MIMO detector under Gaussian and non-Gaussian clutter.

On the Use of Matrix Information Geometry for Polarimetric SAR Image Classification

Polarimetric SAR images have a large number of applications. To extract a physical interpretation of such images, a classification on their polarimetric properties can be a real advantage. However, most classification techniques are developed under a Gaussian assumption of the signal and compute cluster centers using the standard arithmetical mean. This paper will present classification results on simulated and real images using a non-Gaussian signal model, more adapted to the high resolution images and a geometrical definition of the mean for the computation of the class centers. We will show notable improvements on the classification results with the geometrical mean over the arithmetical mean and present a physical interpretation for these improvements, using the Cloude-Pottier decomposition.

Hyperspectral Anomaly Detectors Using Robust Estimators

Anomaly detection methods are devoted to target detection schemes in which no a priori information about the spectra of the targets of interest is available. This paper reviews classical anomaly detection schemes such as the widely spread Reed-Xiaoli detector and some of its variations. Moreover, the Mahalanobis distance-based detector, rigorously derived from a Kellys test-based approach, is analyzed and its exact distribution is derived when both mean vector and covariance matrix are unknown and have to be estimated. Although, most of these techniques are based on Gaussian distribution, we also propose here ways to extend them to non-Gaussian framework. For this purpose, elliptical distributions are considered for background statistical characterization. Through this assumption, this paper describes robust estimation procedures (M-estimators of location and scale) more suitable for non-Gaussian environment. We show that using them as plug-in estimators in anomaly detectors leads to some great improvement in the detection process. Finally, the theoretical contribution is validated through simulations and on real hyperspectral scenes.

SAR Images Refocusing and Scattering Center Detection for Infrastructure Monitoring

Infrastructure monitoring applications can require the tracking of slowly moving points of a certain structure. Given a certain point from a structure to be monitored, in the context of available SAR products where the image is already focused in a slant range - azimuth grid, it is not obvious if this point is the scattering center, if it is in layover or if it is visible from the respective orbit. This paper proposes a refocusing procedure of SAR images on a set of measured points among with a 4D tomography based scattering center detection. The refocusing procedure consists of an azimuth defocusing followed by a modified back-projection on the given set of points. The presence of a scattering center at the given positions is detected by computing the local elevation-velocity plane for each point and testing if the main response is at zero elevation. The refocusing and scattering center detection algorithm is validated on real data acquired with the TerraSAR-X satellite during March-June 2012. The mean displacement velocities of the detected scatterers show good agreement with the in-situ measurements.

Stable Scatterers detection and tracking in heterogeneous clutter by repeat-pass SAR interferometry

This paper presents a new estimation scheme for optimally deriving clutter parameters with high resolution repeat-pass SAR interferometry. The heterogeneous clutter in InSAR data is described by the Spherically Invariant Random Vectors model. Three parameters are introduced for the high resolution InSAR data clutter: the span, the normalized texture and the speckle normalized covariance matrix. The asymptotic distribution of the novel span estimator is investigated.

A SIRV-CFAR adaptive detector exploiting persymmetric clutter covariance structure

This paper deals with covariance matrix estimation for radar detection in non-Gaussian noise modeled by spherically invariant random vector (SIRV). In many applications, it is possible to assume a particular structure for the clutter covariance matrix: this is the case for instance for active systems using a symmetrically spaced linear array or pulse train. In this paper, we propose to use the particular persymmetric structure of the matrix to improve performance in term of detection. In this context, we provide a new adaptive detector and derive its statistical properties as well as its statistical distribution. Moreover, the high improvement of its detection performance is demonstrated on experimental ground clutter data.