Frédéric Pascal (Supelec/L2S) donnera un Crash Course sur le thème des distributions symétriques elliptiques complexes, des M-estimateurs et de leurs applications en traitement du signal.

Complex elliptically symmetric (CES) distributions have been widely used in various engineering applications where non-Gaussian models are called for. In this tutorial, circularly symmetric CES distributions are surveyed, some new results are derived and their applications e.g., in radar and array signal processing are discussed and illustrated with theoretical and real-word examples, simulations and analysis of real radar data.
A particular emphasis is on maximum likelihood (ML) estimation of the scatter (covariance) matrix parameter.  General conditions for its existence and uniqueness, and for convergence of the iterative fixed point algorithm are discussed in detail. Specific ML-estimators for several CES distributions that are widely used in the signal processing literature are discussed in depth, including the complex t-distribution, the K-distribution, the generalized Gaussian distribution and the recently proposed generalized inverse Gaussian distribution. Also the closely related angular central Gaussian distribution is discussed. A generalization of ML-estimators, the M-estimators of scatter matrix, is also discussed and asymptotic analysis is provided. The tutorial also contains new results on regularized (penalized) M-estimators of scatter, which are needed in low sample support scenarios.
Applications of CES distributions and the adaptive signal processors based on conventional and regularized ML- and M-estimators of the scatter matrix are illustrated in diverse applications such as radar detection problems and in array signal processing applications for Direction-of-Arrival (DOA) and beamforming in low sample support case. Also applications in image processing (e.g., hyperspectral imaging, Polarimetric SAR images) are discussed. Furthermore, experimental validation of the usefulness of CES distributions for modelling real radar data is given.

Frédéric Pascal received the Master's degree ("Probabilities, Statistics and Applications: Signal, Image et Networks") with merit, in Applied Statistics from University Paris VII - Jussieu, Paris, France, in 2003. Then, he received the Ph.D. degree of Signal Processing, from University Paris X - Nanterre, advised by Pr. Philippe Forster: "Detection and Estimation in Compound Gaussian Noise" in 2006. This Ph.D thesis was in collaboration with the French Aerospace Lab (ONERA), Palaiseau, France.
From November 2006 to February 2008, he made a post-doctoral position in the Signal Processing and Information team of the laboratory SATIE (Système et Applications des Technologies de l'Information et de l'Energie), CNRS, Ecole Normale Supérieure, Cachan, France.  Between March 2008 and December 2011 (resp. Jan. 2012 – Dec. 2013), he was an Assistant Professor (resp. Associate Professor) in SONDRA, the French-Singaporean laboratory of SUPELEC. In 2012, he obtained a Research Directorship Habilitation (HDR) thesis in Signal Processing from the University of Paris-Sud. From January 2014, Frederic Pascal is a full Professor in the SONDRA laboratory at SUPELEC. His research interests contain estimation, detection and classification in statistical signal processing and radar processing.

Details on website : http://www.lss.supelec.fr/seminaires-automatique/seminar.html

10h-11h   Alain Sarlette,  INRIA Roquencourt :  Engineering controlled quantum systems
Abstract : we give a short introduction to the emerging field of engineering controlled quantum physical devices. For simplicity we focus on the discrete-time approach and on idealized models that are close to actual experimental realizations. We carefully introduce the basic models involved and two feedback stabilization strategies: one equivalent to classical measurement-based control, and the other working as control by interconnection. We illustrate these strategies on a quantum microwave cavity experiment from the Laboratoire Kastler Brossel at ENS Paris.
Related paper : Stabilization of nonclassical states of one- and two-mode radiation fields by reservoir engineering, Phys.Rev.A (2012)

11h-12h   Nicolas Boulant (UNIRS/NeuroSpin/I2BM/DSV CEA) : Design of non-selective refocusing pulses in magnetic resonance imaging at ultra-high field
Abstract : Ultra-High Field MRI at UHF suffers from severe B1 field inhomogeneities which are detrimental to medical diagnosis. Although many powerful flip-angle homogenizing RF pulse designs have been developed to mitigate this effect, there are still numerous and useful MRI applications where the initial input for an RF pulse can be arbitrary, i.e. not necessarily longitudinal, which means that true rotation matrices need to be designed and must perform well regardless of the input magnetization state. This is a very common scenario in quantum computing applications where control schemes are designed to synthetize logic gates to be effective regardless of the input state. For that purpose, the GRadient Ascent Pulse Engineering (GRAPE) method of Khaneja et al (JMR 2005) developed within the framework of NMR quantum computing has been successfully implemented in our laboratory to design arbitrary refocusing pulses. No approximation is made so that all non-linearities and Larmor frequency offsets are fully taken into account. After introducing the theory, I will present experimental data obtained in-vitro (spin-echo) and in-vivo (SPACE, FLAIR) at 7 Tesla which demonstrates the applicability of the technique as well as the correct control of the nuclear spins

LUCA ZACCARIAN, A dynamic input allocation paradigm illustrated by applications
Abstract : Typical control systems designs concentrate on plant output specifications and don't always take sufficiently into account specifications related to its input (e.g., coming from saturation phenomenas). This fact is especially relevant when there are degrees of freedom in the plant input selection due to some kind of acuator  redundancy. In this talk we will present a dynamic input allocation scheme that has been proposed roughly five years ago. The core ideas behind the dynamic scheme will be illustrated while discussing its recent application to: elongation and shape control for Tokamak plasmas, control of parallel hybrid electric vehicles, and satellite attitude control.

Biosketch : Luca Zaccarian received the Ph.D. degree from the University of Roma Tor Vergata, Rome, Italy, in 2000 where he then became first Assistant and then Associate professor until 2011. Since 2011 he is Directeur de Recherche at the LAAS-CNRS, Toulouse, France. Since 2013, he also holds a part-time Associate Professor position at the University of Trento, Italy. His main research interests include analysis and design of nonlinear and hybrid control systems and their applications. Luca Zaccarian is an associate editor of the IEEE Transactions on Automatic Control and Systems and Control Letters, was a member of the IEEE-CSS CEB in 2009-2013, is a member of the EUCA-CEB and served as organizing committee and IPC member for several  international conferences. He was a recipient of the 2001 O. Hugo Schuck Best Paper Award given by the AACC and is a Senior Member of  IEEE.

Sergio GALEANI,  On optimal/constrained output regulation for linear fat plants
Abstract : Output regulation is the problem of ensuring that an output is driven to zero despite the influence of an exogenous signal generated by a known system in free evolution (the "exosystem"); such a problem includes as special cases output tracking and disturbance rejection for preassigned classes of references and disturbances. In classic results, when the plant is fat (i.e. it has more inputs than outputs) it is just squared down a priori in order to ensure that a unique, linear regulator exists, thus disregarding the infinitely many available alternatives. In this talk, starting from ideas related to input allocation and focusing on linear fat plants in the presence of  constraints or performance criteria, it is shown that better output regulators can be obtained by scheduling inside the linear variety of linear regulators, and that optimal regulators are nonlinear even in very simple cases.

Biosketch : Sergio Galeani received the PhD degree from the University of Roma Tor Vergata, Rome, Italy, in 2002, where he works as an Assistant professor since 2004. His main research interests include analysis and design of linear and nonlinear control systems ; his more recent work has been focused on hybrid output regulation and on nonlinear optimal output regulation for linear fat systems. Sergio Galeani is a member of the IEEE-CSS CEB since 2010, and served as organizing committee and IPC  member for several international conferences.

In modern high resolution radar systems, many statistical analysis have proved that the typical radar disturbance, the clutter, is not Gaussian-distributed. In this background the detectors designed for working in Gaussian clutter suffer a huge increase of the false alarms and losses in the performance. For this reason in the last 25 years much attention has been devoted to the modeling of the clutter echoes and to the design of optimum and suboptimum detectors for target detection in non-Gaussian background. Aim of this talk is to describe the state-of-the-art approaches to the coherent target detection in such background.

After a brief description of the modern statistical and spectral models for high resolution sea and ground clutter, with particular attention to the popular compound-Gaussian model, the talk will focus on the design of coherent optimum and suboptimum detectors and to their performance derivation and analysis against such compound-Gaussian background. Different interpretations of the various detectors are provided to highlight the relationships and the differences among them.

The talk will be completed by the analysis of the adaptivity in the detector design, with particular attention to the disturbance covariance matrix estimation. Some new results on naive, robust and fully-adaptive estimation approaches will be provided together with some hints on possible future research directions. 

non-gaussian_detection.pdf