Scientific Activities

My research interests and activities include estimation theory in statistical signal processing and applications in radar, with a particular focus on robust estimation and performance analysis.

Thesis abstract:

One of the objectives of statistical signal processing is the extraction of useful information from a set of data and a statistical model. For example, most of the methods for detecting/localizing targets in radar generally require the estimation of the covariance matrix. With the emergence of high-resolution systems, the use of a Gaussian model is no longer suited and therefore leads to performance degradations. In addition, prior information can be obtained by a prior study of the system, such as the structure of the covariance matrix. Taking them into account then improves the performance of the processing methods. First, we introduce new robust structured estimators of the covariance matrix, based on the family of elliptical distributions and the class of M-estimators. We analyze the asymptotic performances of the latter and we conduct a sensitivity analysis by considering the possibility of mismatches on the statistical model. Secondly, we propose a reformulation of the target detection problem using sparse subspace clustering techniques. We then study some theoretical properties of the optimization problem and we apply this methodology in a scenario of target detection in presence of jammers.

Keywords:

Array processing, Multivariate statistics, Robust and structured estimation, Performance analysis, Sparse subspace clustering