Curriculum Vitae

 

Dr OVARLEZ Jean Philippe

Research engineer in ONERA, Dr. in Signal Processing, HDR

Born March, 26th, 1963 in Denain, France

French Citizen

Three children

SHORT BIO:

Principle Scientist in ONERA DEMR Department (Signal Processing Unit)

Ph.D. student from 1988 to 1991 in the Radar System Division at ONERA (the French Aerospace Lab), I joined ONERA as scientific researcher in January 1992 and defended my Ph.D. Thesis in April 1992. During the reorganization of ONERA in September 1997, I joined the Signal Processing unit of the Radar and Electromagnetism Department in the Physics Branch. In September 2002, I became the leader of the ALBATROS Project which led to the development, control and emphasized of new techniques and research made for the ONERA DEMR RAMSES airborne system (SAR image algorithms, interferometry, polarimetry, thesis supervisions, software development, etc). In August 2003, I became member of the Scientific Committee of the ONERA Physics Branch.  I was elected President of the DEMR Scientific Advisory Board in January 2006 and again in January 2007. In January 2008, I joined the French-Singaporean SONDRA Lab on a part time basis (two days per week) to supervise Signal Processing activities. I was attached scientist in january 2010 for one year to DSO National Laboratories in Singapore. In 2011, I obtained a Research Directorship Habilitation (HDR) thesis in Signal Processing from the University of Paris-Sud and my qualification to the University Professor position. In 2015, I became member of Special Area Team (SAT) in Theoretical and Methodological Trends in Signal Processing (TMTSP), EURASIP. My research interests are centered in the topic of Statistical Signal Processing for radar and SAR applications such as Time-Frequency, imaging, robust detection and parameters estimation.  

COMPETENCE DOMAINS

  1. Puce Spectral Analysis, Source Localization

  2. Puce Time-Frequency Analysis, Time-Scale Analysis, Wavelet

  3. Puce Radar, Sonar

  4. Puce Detection, Parameters Estimation

  5. Puce Array Signal Processing

  6. Puce Radar Imaging (ISAR, SAR), Radar Imaging in Laboratory

  7. Puce Statistics (PDF estimation, Bayesian Methods, Higher Order Statistics, Statistical Tests)

  8. Puce Recognition Methods

  9. Puce Algorithms and scientific software development (C, Fortran, Matlab, Maple, LaTeX, etc)

  10. Puce Supervision of Transversal Projects, contacts with industries and universities

  11. Puce Languages: Russian, English (spoken, written)

Ph.D. THESIS:

"The Mellin Transform: a Tool for Broadband Signals Analysis", Ph.D. thesis from Paris 6 University, April 1992.


Many signal analysis methods have a reasonable theoretical significance only for the class of narrow-band signals, which is connected with the group of time and frequency translations. This is the case, for example, of the Time-Frequency Distributions of the Cohen's class, but also, of the Woodward Ambiguity Functions, which thus, found a limitation in their use conditions. These two typical examples have, however, an extension in the broadband domain: the Bertrand's Affine Time-Frequency Distributions and the Wideband Ambiguity Functions. These forms are related to the affine group transformations which act on the signals by time dilations and translations. This feature complicates their implementation and suggests the use of a Mellin Transform in order to process dilations efficiently. This work is devoted to the study of this new transform, which has many similarities with the Fourier's one (physical interpretation of the Mellin variable in the time-frequency plane, properties, discrete transform, sampling theorem). All the new applications, theoretical results or the algorithms developed with the help of this transform are presented: fast computation of the Affine Time-Frequency Distributions and their smoothing versions (Wavelet Transform), fast computation of the Wideband Ambiguity Functions, the theoretical development of the Cramer-Rao Bounds for the velocity and time-delay estimates in the broadband case and finally, the Broadband Radar Imaging using Dimensionalized Wavelet Transform.

EDUCATION


  1. Puce Qualification to the Professor of the University Position (61 section), January 2012

  2. Puce Habilitation à Diriger les Recherches (Paris XI University), 14 February 2011

  3. Puce 1988-1992 Ph.D. Degree in Physics from Paris 6 University in the Signal Processing specialization obtained with merit (Très Honorable)

  4. Puce 1986-1987 Master’s Degree in Automatic Control and Signal Processing in LSS (Signals and Systems Lab - Supelec - 91 Gif sur Yvette, France) obtained with merit (Bien)

  5. Puce 1984-1987 ESIEA Electrical Engineering Degree (École Supérieure d'Informatique, Électronique, Automatique - 9 Rue Vésale, Paris 5, France) obtained with merit (Très Bien)

  6. Puce 1981-1984 Math Sup and Math Spe (Mathematics Option): school preparing to “Grandes Ecoles”

  7. Puce 1981 Baccalauréat of Science (C serie) (Lycée Henri Wallon - 59300 Valenciennes, France)

WORK EXPERIENCES
  1. Puce Jan 2010 One year Attached Associate Researcher from ONERA to DSO National Laboratories in Singapore (DGA funding)

  2. Puce Jan 2008 Attached Associate Researcher from ONERA to the French-Singaporean SONDRA Lab of Supelec on a part time basis (two days per week) in charge of Signal Processing research activities.

  3. Puce 1992-2010 Currently Principal Scientist 2 in ONERA (since 2003) in the Signal Processing unit of the Radar and Electromagnetism department DEMR

  4. Puce 1988-1992 Ph.D. Thesis from Paris 6 University defended in April 1992 in the Radar Systems Division in ONERA entitled "The Mellin Transform: A Tool for WideBand Signal Analysis"

  5. Puce 1987-1988 Military Education and Training in August 1987 in the SAMAN (Toussus-Le-Noble, France) Aeronautical Military Base as scientific researcher (courses in mathematics, physics and Russian)

  6. Puce 1987 CSEE (Compagnie des Signaux et d'Entreprises Electriques) and LSS (Signals and Systems Lab, Supelec - Gif sur Yvette, France). Master Internship: Speech Recognition in Highly Noisy Environment

  7. Puce 1986 INRIA (French National Institute for Research in Computer Science and Control) - ESIEA Project (March to December): Numerical Filtering and Kalman Filtering

HDR:

"Some Contributions to Methods of Analysis, Detection and Estimation for Radar and SAR Imaging", HDR from Paris 11 University, February 2011.


  This HDR thesis presents a synthesis of works I have been conducting since more than twenty years in the Signal Processing Unit (Electromagnetism and Radar Department) of ONERA, the French Aerospace Lab. All these works have mainly been devoted to the Signal Processing techniques for radar and imaging radar.

  The fist part comes logically from my Ph.D. works conducted on the Mellin Transform and consists in extending Time Frequency Distributions for radar imaging. Usually used for analyzing non stationary signals, these techniques allow to study the coloration and the anisotropy of the scatterers in a SAR or ISAR image. Mainly based on sub-band and sub-look decompositions, these Time-Frequency distributions allow also to study the angular and spectral non-stationarity of the polarimetric mechanisms of scatterers in polarimetric SAR images but also to improve the quality of the interferometric coherency estimate (INSAR) and of the polarimetric coherency estimate (POLINSAR).

  The second part concerns radar detection and estimation in non homogeneous and/or non-Gaussian environment. The compound Gaussian SIRV (Spherically Invariant Random Vectors)modelling, allows nicely to extend all the classical detection schemes based only on the Gaussian assumption. Jointly used to robust and powerful estimators of the unknown environment parameters (e.g. covariance matrix), these modelling techniques can be applied to multi channels radar detection (array processing for source localization, STAP (Space Time Adaptive Processing), MIMO, interferometry, polarimetry, hyperspectral imaging, ...).

  In fact, these two research axes that have been built and exploited independently can be jointly be used to solve new problems that are developed in my research perspectives. All the applications derived from these two axes are quite numerous: mono and multi channels SAR images analysis (detection, segmentation, classification), moving target detection in SAR images, hyperspectral imaging, change detection, ...