Source: http://emmanuelduflos.ec-lille.fr/
Timestamp: 2019-04-21 17:01:28+00:00

Document:
I am Full Professor at Ecole Centrale de Lille where I am the Deputy Director and Director of Research. I mainly teach at graduate level: bayesian estimation, Sequential Monte Carlo and MCMC methods.
My research activity focus on statistical signal processing, bayesian modeling and analysis and multi-objects filtering. One of my major aim is to show the benefits of Bayesian Non Parametric methods in the field of signal processing. I’ve showed how this framework can be successfully use to mitigate multipath effects in GNSS/GPS localizations algorithms.
"Habilitation à Diriger des Recherches (HDR)"
- Personal research activity in Statistical Signal Processing and Bayesian Analysis with a main application to GPS localization enhancement.
- Teaching: Statistical Signal Processing and Bayesian Analysis.
- Participation to the creation of the INRIA project team SequeL devoted Sequential Learning. SequeL hosted by the INRIA Lille Nord Europe.
- Head of the last year specialization program in Signal Processing and Automatic Control.
1. The development of Bayesian non parametric (BNP) methods for signal processing. My team and I have been the first to propose BNP methods for estimation purpose in non gaussian dynamical systems. The seminal publication on BNP in IEEE Transactions in Signal Processing has been cited 80 times (Google scholar) since its publication in 2008. Such a framework has been used successfully to derive new state Bayesian estimators to mitigate both multipath noise in GNSS localization and impulsive (alpha-stable) noise.
2. The development of multisensor multi target tracking methods based on finite random sets (like the Probability Hypothesis Density (PHD) filter) . This activity is more recent.
Numerous problems in signal processing may be solved efficiently by way of a Bayesian approach. The use of Monte-Carlo methods let us handle non linear, as well as non Gaussian problems. In their standard form, they require the formulation of densities of probability in their parametric form. For instance, it is a common usage to use Gaussian likelihood, because it is handy. However, in some applications such as Bayesian filtering, or blind deconvolution, the choice of a parametric form of the density of the noise is often arbitrary. If this choice is wrong, it may also have dramatic consequences on the estimation quality. To overcome this shortcoming, one possible approach is to consider that this density must also be estimated from data. A general Baysesian approach then consists in defining a probabilistic space associated to the possible outcomes of the object to be estimated. Applied to density estimation, it means that we need to define a probability measure on the probability density of the noise : such a measure is called a random measure. The classical Bayesian inference procedures can then been used. This approach being by nature non parametric, the associated frame is called Non Parametric Bayesian.
In particular, mixtures of Dirichlet processes provide a very powerful formalism. Dirichlet Processes are a possible random measure and Mixtures of Dirichlet Processes are an extension of well-known finite mixture models. The class of densities that may be written as a mixture of Dirichlet processes is very wide, so that these are really fit to very large amount of applications. Given a set of observations, the estimation of the parameters of a mixture of Dirichlet processes is performed by way of a Monte Carlo Markov Chain (MCMC) algorithm. Dirichlet Process Mixture are also widely used in clustering problems. Once the parameters of a mixture are estimated, they can be interpreted as the parameters of a specific cluster defining a class as well. Dirichlet processes are well known within the machine learning community and its potential in statistical signal processing still need to be developped.
In the general multi-sensor multi-target Bayesian framework, an unknown (and possibly varying) number of targets whose states are observed by several sensors which produce a collection of measurements at every time step. Well-known models to this problem are track-based models such as the joint probability data association (JPDA) or joint multi-target probabilities such as the joint multi-target probability density. Common difficulties in multi-target tracking arise from the fact that the system state and the collection of measures from sensors are unordered and their size evolve randomly through time. Vector-based algorithms must therefore account for state coordinates exchanges and missing data within a unknown time interval. Although this approach is very popular and has resulted in many algorithms in the past, it is not the optimal way to tackle the problem since the sate and the data are in fact sets and not vectors.
The random finite set theory provides a powerful framework to cope with these issues. Mahlerís work on finite sets statistics (FISST) provides a mathematical framework to build multi-object densities and derive the Bayesian rules for state prediction and state estimation. Randomness on object number and their states are encapsulated into random finite sets (RFS), namely multi-target(state) sets and multi-sensor (measurement) set. The objective is then to propagate the multitarget probability density by using the Bayesian set equations at every time step. Unfortunately, although these equations may seem similar to the classical single-sensor/single-target Bayesians equations, they are generally untractable because of the presence of the set integrals. For, a RFS is characterized by the family of its Janossy densities and not just by one density as it is the case with vectors. To solve this problem, Mahler introduced the PHD, defined on single-target state space. The PHD is the quantity whose integral on any region is the expected number of targets inside this region. Mahler proved that the PHD is the first-moment density of the multi-target probability density.
4.M. de Vilmorin, E. Duflos, P. Vanheeghe, « Radar Optimal Times Detection Allocation in Multitarget Environment », IEEE Systems Journal, 2009, 3,2 (Jan., 2009) pp. 210-220.
12.F. CARON, E. DUFLOS, D. POMORSKI, P. VANHEEGHE GPS/IMU Data Fusion using Multisensor Kalman Filtering: Introduction of Contextual Aspects Information Fusion vol. 7(2), pp. 221-230, 2006.
13.S. PERRIN, E. DUFLOS, P. VANHEEGHE, A. BIBAUT Multisensor Fusion in the Frame of Evidence Theory for Landmines Detection IEEE Transactions on Systems, Man, and Cybernetics, Part C : Applications and Review Vol 34, N° 4, (2004), pp 485 – 498.
14.S. PERRIN, E. DUFLOS, F. NIVELLE, P. VANHEEGHE A Matlab graphic interface for multisensor data analysis and algorithms performances evaluation in the frame of antipersonnel mines detection Studies in Informatics and Control Vol. 5, N° 3, (2000), pp 223 - 232.
15.E. DUFLOS, P. PENEL, P. VANHEEGHE 3 D Guidance Law Modeling IEEE Transactions on Aerospace and Electronic Systems Vol. 35, N° 1, (1999), pp 72-83.
16.E. DUFLOS, P. PENEL, P. VANHEEGHE, P. BORNE Another point of view on proportional navigation Mathematical Problems in Engineering, Theory, Methods and Applications Vol. 4, N° 3, (1998), pp 201-231.
17.E. DUFLOS, P. VANHEEGHE, Discontinuous parameter estimation in pure proportional navigation trajectories System Analysis - Modelling - Simulation, a journal of mathematical modelling and simulation in systems analysis Vol. 27 (1996), pp 315-332.
26.Juliette Marais, N. Viandier, A. Rabaoui Emmanuel Duflos, GNSS multipath bias models for accurate positioning in urban environments, ITST 2010, 9-11 nov. 2010, Kyoto, Japon.
27.Juliette Marais, Emmanuel Duflos, N. Viandier, D.F. Nahimana, A. Rabaoui, Advanced signal processing techniques for multipath mitigation in land transportation environment, ITSC 2010, session spéciale "Advances in Positioning and Map-Matching and Applications", Sept. Madère, 2010.
28.Nicolas Viandier, Asma Rabaoui, Juliette Marais, Emmanuel Duflos, GNSS pseudorange error density tracking using Dirichlet Process Mixture, Fusion 2010, 26-29 Juillet, Edinburgh, UK.
29.Nicolas Viandier, Asma Rabaoui, Juliette Marais and Emmanuel Duflos, Studies on DPM for the density estimation of pseudorange noises and evaluations on real data, IEEE PLANS, Palm Springs, -6-9 May, 2010.
32.Nicolas Viandier, Fleury Nahimana, Juliette Marais, Emmanuel Duflos, GNSS Accuracy enhancement in urban environments based on error modeling and sequential Monte Carlo, Workshop Localisation précise et sure, Paris, le 16 juin 2009, 6p.
33.Nicolas Viandier, Asma Rabaoui, Juliette Marais and Emmanuel Duflos Enhancement of Galileo and multi-constellation accuracy by modeling pseudorange noises, The 9th International Conference on ITS Telecommunications (ITST), Lille, 20-22 oct. 2009, pp 459-464.
34.Asma Rabaoui, Nicolas Viandier, Juliette Marais and Emmanuel Duflos On the use of Dirichlet Process Mixtures for the modelling of pseudorange errors in multi-constellation based localization, The 9th International Conference on ITS Telecommunications (ITST), Lille, 20-22 oct. 2009, pp 465-470.
40.NAHIMANA D-F, MARAIS J., DUFLOS E. Pseudodistance error modelling in urban environment Proceedings of European Navigation Conference (ENC) 2007, Geneva.
41.NAHIMANA D-F., DUFLOS E., MARAIS J., A Jump Markov System for Modelling a Realistic Error Model Depending on Satellite Reception State in Urban Environment Proceedings of ION GNSS 2007, Fort Worth, Texas.
42.NAHIMANA D-F, MARAIS J., DUFLOS E. The best use of available satellites for the best GNSS localisation in an urban environment Proceedings of European Navigation Conference (ENC) 2006, Manchester.
43.F. Caron, M. Davy, E. Duflos, P. Vanheeghe. Particle filtering for multipath effects reduction in land vehicle positioning . 4th CESA Multiconference on "Computational Engineering in Systems Applications" (CESA 2006) , Beijing, China, October 4-6, 2006.
44.F. Caron, M. Davy, A. Doucet, E. Duflos, P. Vanheeghe. Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures. International Conference on Information Fusion (FUSION'06), Florence, Italia, July 10-13, 2006.
45.F. Caron, B. Ristic, E. Duflos, P. Vanheeghe. Least Committed basic belief density induced by a multivariate Gaussian pdf . International Conference on Information Fusion (FUSION'06), Florence, Italia, July 10-13, 2006.
46.F. Caron, S. Navabzadeh Razavi, J. Song, P. Vanheeghe, E. Duflos, C. Caldas, C. Haas. Models for locating RFID nodes. Joint International Conference on Computing and Decision Making in Civil and Building Engineering (ICCCBE'06), Montreal, Canada, June 14-16, 2006.
47.F. Caron, P. Smets, E. Duflos, P. Vanheeghe. Multisensor data fusion in the frame of the TBM on reals. Application to land vehicle positioning. International Conference on Information Fusion (FUSION'05), Philadelphia, PA, USA, July, 2005.
48.F. Caron, E. Duflos, P. Vanheeghe. Introduction of contextual information in a multisensor EKF for autonomous land vehicle positioning. IEEE International Conference on Networking, Sensing and Control (IEEE ICNSC'2005), Tucson, AZ, USA, March, 2005.
49.D. Potin, E. Duflos , P. Vanheeghe Time Response Estimation of buried Landmines by using an online abrupt changes detection algorithm. IMACS (IMACS 2005) Paris, France, July, 2005, paper on CD-ROM, 6 pages.
50.F. Caron, P. Vanheeghe, E. Duflos. On the use of χ² statistics of the Kalman filter as contextual information in multisensor Kalman filtering. International Conference on Nonlinear Problems in Aviation and Aerospace (ICNPAA'2004), Timisoara, Romania, June, 2004.
51.M. de VILMORIN, E. DUFLOS, P. VANHEEGHE Optimal sensor management strategies : temporal resources allocation . CESA’2003 IMACS Multiconference Computational Engineering in Systems Applications , Lille, France, July, 2003,Proceedings on CD rom, S3-R00-0167 :S3-TP-1-F1 (6 pages).
52.P. VANHEEGHE, E. DUFLOS, P. E. DUMONT, V. NIMIER Sensor management with respect to danger level of targets" . 40th IEEE conference on Decision ans Control. , Orlando, Florida (USA), December 2001, Proceedings pp 4439 – 4444.
60.E. DUFLOS, P. VANHEEGHE, P. BORNE Fuzzy fusion operator for mines detection . IEEE International Conference on Systems Man, and Cybernetics , San Diego (USA), October 1998, Proceedings Vol. 2, pp 1156 - 1161.
61.E. DUFLOS, P. VANHEEGHE Dempster - Shafer theory based fusion operator for mines detection . International Conference on Computational Engineering in Systems Applications, CESA'98 IMACS Multiconference, Symposium on Signal Processing and Cybernetics , Nabeul - Hammamet (Tunisia), April 1998, Proceedings Vol. 4, pp 76 - 81.
62.P. VANHEEGHE, E. DUFLOS, R. LE LETTY Control of an Ultrasonic Motor with a Digital Signal Processor . IEEE International Conference on Systems Man, and Cybernetics , Orlando (USA), October 1997, Proceedings Vol. 4 , pp 3626 - 3629.
63.E. DRUON, E. DUFLOS, V. BOULET, D. WILLAEYS, P. VANHEEGHE Comparison of different fuzzy algorithms in a curve recognition problem . IEEE International Conference on Systems Man, and Cybernetics , Vancouver (Canada), October 1995, Proceedings vol 5, pp 4107-4112.
64.E. DUFLOS, E. DRUON, V. BOULET, P. PENEL, P. VANHEEGHE General 3D Guidance Law Modeling . IEEE International Conference on Systems Man, and Cybernetics. , Vancouver (Canada), October 1995, Proceedings vol 3, pp 2013-2018.
65.E. DUFLOS, E. DRUON, V. BOULET, P. VANHEEGHE, P. PENEL P.Y. ARQUES Consideration about pure proportional navigation . IEEE International Conference on Systems Man and Cybernetics. , San Antonio (USA), October 1994, Proceedings vol 3, pp 2556-2560.
66.E. DRUON, E. DUFLOS, V. BOULET, D. WILLAEYS P. VANHEEGHE An estimation environment . IEEE International Conference on Systems Man, and Cybernetics , San Antonio (USA), October 1994, Proceedings vol 3, pp 2550-2555.
67.V. BOULET, E. DRUON, E. DUFLOS, P. VANHEEGHE and P. BORNE Optimal control in estimation problems . IEEE International Conference on Systems Man and Cybernetics , San Antonio (USA), October 1994, Proceedings vol 3, pp 2658-2662.
68.V. BOULET, E. DRUON, E. DUFLOS, P. BORNE, D. WILLAEYS and P. VANHEEGHE Target estimation techniques . IMACS Symposium on Mathematicals Modeling. , Vienna, Austria, February 1994, Proceedings vol 2, pp 277-278.
69.E. DUFLOS, M.P. BOYER, Y. COQUARD and P. VANHEEGHE Introduction of neural network in the decentralized detection problems . IEEE International Conference on Systems Man and Cybernetics. , Le Touquet, France, 1993, vol 3, pp 650-655.
70.E. DUFLOS, M. de VILMORIN, P. VANHEEGHE Détermination de Stratégie de Gestion Dynamique Optimale pour un Radar à Balayage Electronique" Revue Française de Traitement du Signal Vol 19, N° 2, (2002), pp 59 – 73.
71.P. VANHEEGHE, V. BOULET, P. BORNE, E. DRUON, D. WILLAEYS, E.DUFLOS, P. Y. ARQUES, P. PENEL, B. LACHESE Inversion de loi de guidage en interaction avec une flotte Revue Scientifique et Technique de la Défense N° 50, (2000), pp 99 – 112.
72.E. DUFLOS, P. VANHEEGHE, P. PENEL, P. BORNE A Probabilistic Method to Estimate the Target of a Missile Nonlinear Problem in Aviation and Aerospace Edited by S. Sivasundaram, Gordon and Breach Science Publishers, London, U.K,1999, pp 209-220.
73.N. Jaoua, E. Duflos, F. Septier, P. Vanheeghe Estimation bayésienne non paramétrique de l'état et du bruit impulsif dans les systèmes dynamiques non linéaires, ACtes du GRETSI 2013, Brest, Septembre 2013 !
76.F. Caron, M. Davy, E. Duflos, P. Vanheeghe. Fusion de capteurs potentiellement défaillants par filtrage particulaire. Colloque GRETSI sur le traitement du signal et des images, Louvain-La-Neuve, Belgium, September 6-9, 2005.
77.M. de VILMORIN, E. DUFLOS, P. VANHEEGHE estion de capteurs pour la detection optimale de cibles aériennes : allocation temporelle . colloque GRETSI sur le traitement du signal et des images Paris, Septembre 2003, Actes sur CD rom, vol. III, pp 197-200.
78.F. Caron, M. Davy, E. Duflos, P. Vanheeghe. Fusion de capteurs potentiellement défaillants par filtrage particulaire.. Réunion GDR-ISIS: Localisation et Navigation, Paris, 10 février 2006.
79.F. Caron, C. Haas, P. Vanheeghe, E. Duflos. Modélisation de mesures de proximité par la théorie des fonctions de croyance. Application à la localisation de matériaux de construction équipés d'étiquettes RFID.. La théorie des fonctions de croyance : de nouveaux horizons pour l'aide à la décision, Journée d'étude de la SEE, Paris, 8-9 décembre 2005.
80.F. Caron, M. Davy, E. Duflos, P. Vanheeghe. Fusion de capteurs non fiables par filtrage particulaire. Application au positionnement 2D d'un véhicule terrestre. Poster présenté au séminaire PEPSAT (Pôle de compétence Européen sur le Positionnement Satellitaire Appliqué à la mobilité Terrestre) à l'Université du Littoral et de la Côte d'Opale, Calais, Mai 2005.
81.F. Caron, M. Davy, E. Duflos, P. Vanheeghe. Fusion de capteurs non fiables par filtrage particulaire. Colloque de la Recherche de l'Intergroupe des Écoles Centrale (CRIEC'2005), Ecole Centrale de Lille, Mai 2005.
82.F. Caron, P. Smets, E. Duflos, P. Vanheeghe. Application de la fusion de données pour le localisation de véhicules terrestres. Réunion francophone sur les fonctions de croyance, Compiègne, Mars 2005.

References: V. 
 V. 
 V. 
 V. 
 V. 
 V.