Archive
Jean-Marc LE CAILLEC
Biographie
Jean-Marc Le Caillec is a professor with the IMT Atlantique (Ecole Nationale Superieure des Télécommunications de Bretagne, Telecom Bretagne) in the 2IP (Information and Image Processing) department. He obtained the degree of Engineer in Telecommunications in 1992 from Telecom Bretagne. He received the Ph.D. degree in Mathematics and Signal Processing of the University of Rennes I in 1992. From 1997 to 1999, he worked for Thomson AirSys (now Thales AirSys). He joined Telecom Bretagne as associate professor in 1999 and became full professor in 2007. From 2014 to 2020, he was in charge of the division CID (Knowledge, Information, Decision) of the Lab-STICC (Laboratory of sciences and technics for information, communication and knowledge, UMR 6285). His research activities are in the framework of the "Science and Techniques for Engineers" and in particular in the signal processing and image domains. His activities are at the crossroad of several items: nonlinear systems, non Gaussian statistics, sea surface remote sensing and financial, time series modelling. These four themes are complementary (as seen on each topic page). In fact, the transfer function between the sea surface and the SAR (synthetic aperture radar) or HF radars is nonlinear due to the nonlinearity of fluid mechanism equations. Nonlinear system can be studied by using Higher Order Statistics (HOS) and underlying polynomial models of nonlinear systems. These methods have been applied to extract geophysical information from sea surface remote sensing data or the defocusing problems of buried target images by a low frequency SAR due to the nonlinear variation of the soil mositure (Richard’s equation) and then of the dielectric constants. He initiated works on decision fusion (time series modelling/portfolio management) as well as on homogeneous/heterogeneous data fusion at low level (eg Sonar/Sonar, Lidar/Optic, SAR/Symbolic data registration) or high level (methods for evaluating information quality extracted by a fusion process). Always to take into account the physical processes involved in the data acquisition and remote sensing image formation, he is developing eXplainable Artificial Intelligence approaches for active sensors (Sonar, Radar). He has been an international expert for the National Science Fundation (USA) and Fonds de recherche du Québec - Nature et technologies (Québec, Canada) and obvioulsy several national agencies. He is an associate professor with University Laval (Québec, Canada). His PhD students won the best student paper award at Mikon 2006, Ocean's 2014 and PIA19. He represented France in the former NATO group SET-208 on the signal fusion for ground penetrating radars. He is member of the scientific committee of LATERAL (joint lab between Lab-Sticc and Thales LAS) and CORMORAN (joint lab between Lab-Sticc and Thales DMS). He is author and co author of more than forty papers and book chapters, half as first author.
Nonlinear and non stationnary time series modelliing
This topic can be divided into two subtopics
1) Nonlinearity detection
-Designing a hypothesis testing for nonlinearity (i.e. artifacts) detection in observed time series based only on bispectrum (and comparing with existing approaches) [A5].
-Deriving conventional bispectrum estimates statistics for multidimensional signals, e.g. SAR images [A9].
-Designing a hypothesis testing for nonlinearity detection for finite correlation length time series (unlike AR models) [A 13].
-Designing a topology for merging several Hypothesis Testing for nonlinearity detection in small time series to robustify the decision [A18].
2) Nonlinear system identification and inversion
-Extending the Wiener-Hoff equations to Nonlinear AutoRegressive models as well as to second order Volterra models [A2].
-Proving that second order Hammerstein models with finite [A 4] or infinite [A12] order kernels model can be blindly identified from the first, third and fourth order statistics (thus valid for times series disturbed by gaussian noise) and designing the corresponding identification algorithm.
-Proving that second order Wiener models with finite order kernels can be blindly identified from the first, third and fourth order statistics under the condition of minimal phase kernels and designing the corresponding identification algorithm [A15].
-Designing an algorithm to invert nonlinear (polynomial) systems for which the kernel exhibits transmission zeroes either by regularizing the corresponding frequencies (Tikhonov) [A7] or retrieving the vanished components from the second order kernel output [A15].
-Designing an algorithm releasing the usual limiting assumptions of the Threshold Autoregressive model identification algorithms (in particular the knowledge on the switching condition values/shape) by inverting the AR parameters/Switching condition identification steps [A 38].
-[A 2] J-M Le Caillec R Garello, "Nonlinear System Identification Using Autoregressive Quadratic Models" in Signal Processing, Vol. 81, no 3, March 2001, pp 357-379.
-[A 4] J-M Le Caillec R Garello, "Time Series Nonlinear Modelling : A Giannakis Formula Approach" in Signal Processing, Vol. 83, no 8, August 2003, pp 1759-1788.,
-[A 5] J-M Le Caillec et R Garello, "Comparison of Statistical Indices Using Third Order Spectra for Nonlinearity Detection" in Signal Processing, Vol. 84, no 3, March 2004, pp 499-525
-[A 7] J Inglada, J-M Le Caillec R Garello, "Inversion of Imaging Mechanism by Regularization of Inverse Volterra Models" in Signal Processing, Vol. 84, no 6, June 2004, pp 1021-1034.
-[A 9] J-M Le Caillec R Garello, "Asymptotic Bias and Variance of Conventional Bispectrum Estimates for 2-D Signals" in Multidimensional Signal and System Processing, Vol. 16, no 1, January 2005, pp 49-84.
-[A 12] J-M Le Caillec "Time Series Modeling by Hammerstein Series: Hypothesis Testing and Identification Using Higher Order Spectra" in IEEE Transactions on Signal Processing Vol. 56 no 1, January 2008, pp 96-110
-[A 13] J-M Le Caillec "Hypothesis Testing for Nonlinearity Detection Based on an MA model", in IEEE Transactions on Signal Processing. Vol. 56 no 2, February 2008, pp 816-821
-[A 15] J-M Le Caillec “Inversion of Second Order Volterra model based on the identification of Second Order Wiener model” in Signal Processing. Vol. 91 no 11, November 2011, pp 2541-2555
-[A 18] J-M Le Caillec J Montagner “Fusion of Hypothesis Testing for nonlinearity detection in small time series” Signal Processing, Vol 93, no 5, May 2013, pp 1295-1307
-[A 38] J-M Le Caillec, “Threshold autoregressive model blind identification based on array clustering” in Signal Processing, Vol 184, 2021.108055
Active sensor signal processing and Data/Decision fusion
This topics can be divided into three subtopics
1) Data/decision fusion
-Designing a topology for merging several Hypothesis Testing for nonlinearity detection in small time series to robustify the decision [A18].
-Designing an algorithm for pairing patches of Sonar images based on several statistical criteria derived for non-gaussian data (Mutual Information) [A16] for mosaicking purpose, fusion of the backscattered sonar wave characteristics to identify sea bed vegetation [A17] and finally for AUV guidance [A 23].
-Analytically defining a modular scheme to evaluate the quality of a general fusion process [A25].
-Designing a hypothesis testing to detect whether data or signals contain shared information before merging [A 31].
-Designing an algorithm for complex shape building extraction by fusion of multi temporal optic images and Lidar data [A35-A36]
2) Signal Processing for Radar and Sonar
-Deriving/simulating the effective bandwidth/resolution reduction in the range compression step due to the lossy and dispersive characteristics of the soil for Airborne ground Penetrating Radars [A14-A20-A33].
-Deriving/simulating the effective bandwidth/resolution reduction for a Frequency Modulated Continuous Wave for High Frequency Surface Wave Radar over the sea [A19]
-3-D Reconstruction of complex sea bed scenes from sonar data in shallow waters by tracking multipath backscattering [A26]
-Deriving the optimal subarray baselines in a ULA for optimal ambiguity removal for interferometry [A29]
3) Spatial Oceanography
-Proving that azimuthal wave components in the SAR spectrum of the sea surface mapping are second order artifacts due the sensor that do not exist in the sea surgface spectrum and explaining the generation of these artifacts [A1-A3-A11],
-Applying the nonlinear identification algorithm for estimating the pycnocline depth of an internal wave from SAR images [A10-A42] valid whatever the underlying internal wave propagation model.
-Monitoring the shore with SAR data [A8]
-[A 1] J-M Le Caillec, R Garello et B Chapron "Two Dimensional Bispectral Estimates from Ocean SAR Images" Nonlinear Processes in Geophysics, Vol. 3, no 9, September 1996, pp 196-215.
-[A 3] J-M Le Caillec, R Garello et B Chapron "Analysis of the SAR Imaging Process of the Ocean Surface Using Volterra Models" IEEE Journal of Oceanic Engineering, Vol. 27, no 3, July 2002 pp 675-699.
-[A 8] H Dupuis, V Marieu, V Lafon, N Durand, P Dreuillet, R Garello et J-M Le Caillec "Dynamics and Morphodynamics in Sandy coastal Environments. Application to the Aquitanian Coast" EARSeL Proceedings Vol. 3, no. 3, 2004, pp 289-297.
-[A 10] J-M Le Caillec "Study of the SAR Signature of Internal Waves by Nonlinear Parametric Autoregressive Models" IEEE Transactions on Geosciences and Remote Sensing, Vol. 44, no 1, January 2006, pp 148-158.
[A 11] J-M Le Caillec "SAR Remote Sensing Analysis of the Sea Surface: Polynomial Filter Approach" IEEE Signal Processing Magazine Vol. 24 no 4, July 2007, pp 105-107.
-[A 14] J-M Le Caillec, S Redadaa C Sintes B Solaiman M Benslama " Focusing problems of subsurface point scatterer by a low frequency SAR " IEEE Transactions on aerospace and electronic systems Vol 47 no 1 no 438-450
-[A 16] C Chaillou, J-M Le Caillec, D Gueriot, B Zerr, ”Intensity Based block matching algorithm for mosaicing sonar images” IEEE Journal of Oceanic Engineering Vol 46 no 4, October 2011 pp 627-645
-[A 17] C Monpert M Legris C Noel B Zerr, J-M Le Caillec “Studying and modelling of submerged aquactic vegetation environment seen by a single beam echosounder” Proceedings of meetings on Acoustics (POMA) , Vol 17 December 2012, pp 070044
-[A 18] J-M Le Caillec J Montagner “Fusion of Hypothesis Testing for nonlinearity detection in small time series” Signal Processing, Vol 93, no 5, May 2013, pp 1295-1307
-[A 19] J-M Le Caillec L Mandridake “Influence of the waveform on the forward propagation and backscattering of HF radars” IEEE Antennas and propagation. Vol 65 no 5, May 2014, pp 2721-2735
-[A 20] R Kedzierawski, J-M Le Caillec, W Czarnecki “Time-Reversal technique for SAR focusing of buried target: Theoretical improvements and practical limitations” IEEE Signal processing magazine, Vol 31, no 4, july 2014, pp 99-109.
-[A 23] L Bernicola D Gueriot, J-M Le Caillec “A hybrid registration approach combining SLAM and elastic matching for automatic side-scan sonar mosaic” IEEE oceanic engineering society newsletter, Vol. 3, no 3, pp. 42-46, december 2014,
-[A 25] IG Todoran L Lecornu A Khenchaf J-M Le Caillec “A Methodology to Evaluate Important Dimensions of Information Quality in Systems” ACM Journal of Data and Information Quality, Vol 2 article no 11, june 2015
-[A 26] AA Saucan C Sintes T Chonavel J-M Le Caillec “Model-based Adaptive 3D sonar reconstruction reverberating environments” IEEE Transactions on Image Processing, Vol 34, no 10, october 2015 pp 2928-2940
-[A 29] C Sintes KG Foote G Llort-Pujol J-M Le Caillec B Solaiman “Performance Prediction of a Dual-Baseline Radar or Sonar Interferometer Based on a Vernier Critical Value Concept” IEEE Journal of Oceanic Engineering Vol 43 no 4, 2018 pp 1114-1133
-[A 31] J-M Le Caillec “Testing conditional independence to determine shared information in a data/signal fusion process” Signal Processing, Vol 143, february 2018, pp 7-19
-[A 33] R Kedzierawski J-M Le Caillec W Czarnecki “Simulation of subsurface imaging for remote sensing and buried object detection from airborne platform“ European Journal of Remote Sensing Vol 52:1 2019 pp 583-598
-[A 35] TH Nguyen, S Daniel, D Gueriot, C Sintes J-M Le Caillec “ Coarse-to-fine registration of airborne LIDAR data and optical imagery on urban scene” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020 vol 13 pp 3125-3144
-[A 36] TH Nguyen, S Daniel, D Gueriot, C Sintes J-M Le Caillec, “Super resolution based snake model-An unsupervised model for large-scale building extraction using airborne LIDAR and optical image” MDPI Remote Sensing 2020 vol 12 n° 11
-[A 42] M Dessert, M Honnorat, J-M Le Caillec, C Messager, X Carton, “Estimating the pycnocline depth from the SAR signature of Internal waves in the alborean sea” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022 vol 15 pp 9041-9061
Automatic target Recognition and explainable artificial Intelligence
As mentionned by the title
1) ATR
-Calculating the respective performance of C-band/K band UAV borne radars for sea survey [A 40]
-3-D reconstruction of ships over the sea using the Inverse SAR approach [A6] or multipath electromagnetic backpropagation detection [A32] (on raw radar data).
-PHD Multitarget tracking under burst noise with application to sea bottom reconstruction [A 27].
-Deriving the STAP processing performance for Surface wave HF radar over the sea [A30]
-Deriving/simulating the effective bandwidth/resolution reduction in the range compression step due to the lossy and dispersive characteristics of the soil for Airborne ground Penetrating Radars [A14-A20-A33].
2) XIA
-Defining a method to identify the radar backscattering (scattering center) involved in the military target classification by a convolutional neural network and designing the corresponding algorithm [A34-A37]
-[A 6] G Hajduch, J-M Le Caillec et R Garello, "New scheme of ISAR Imaging Process for Ships with Roll, Pitch and Yaw Motions" IEEE Transactions on Aerospace and Electronic Systems, Vol. 40, no 1, January 2004, pp 378-384.
-[A 14] J-M Le Caillec, S Redadaa C Sintes B Solaiman M Benslama " Focusing problems of subsurface point scatterer by a low frequency SAR " IEEE Transactions on aerospace and electronic systems Vol 47 no 1 no 438-450
-[A 20] R Kedzierawski, J-M Le Caillec, W Czarnecki “Time-Reversal technique for SAR focusing of buried target: Theoretical improvements and practical limitations” IEEE Signal processing magazine, Vol 31, no 4, july 2014, pp 99-109.
-[A 27] AA Saucan T Chonavel C Sintes J-M Le Caillec “CPHD-DOA Tracking of Multiple Extended Sonar Targets in Impulsive Environment ” IEEE Transactions on Signal Processing, Vol 64, no 5, march 2016 pp 1147-1160
-[A 30] J-M Le Caillec T Gorski G Sicot N Thomas A Kawalec “Theoretical Performance of Space- Time Adaptive Processing for Ship Detection by High-Frequency Surface Wave Radars” IEEE Journal of Oceanic Engineering, Vol 43 no 1, january 2018, pp 238-257
-[A 32] J-M Le Caillec J Habonneau A Khenchaf “Ship profile imaging using multipath backscattering” MDPI Remote Sensing 2019, Vol 11, N° 7 748
-[A 33] R Kedzierawski J-M Le Caillec W Czarnecki “Simulation of subsurface imaging for remote sensing and buried object detection from airborne platform“ European Journal of Remote Sensing Vol 52:1 2019 pp 583-598
-[A 34] C Beloni N Aouf A Balleri J-M Le Caillec T Merlet, “Pose-informed deep learning method for SAR ATR” IET Radar and Navigation Vol 14, N° 11 Nov 2020 pp 1649-1658
-[A 37] C Beloni A Balleri N Aouf J-M Le Caillec T Merlet, “Explainability of Deep SAR ATR Through Feature Analysis” EEE Transactions Aerospace and Electronic Systems vol 57 N° 1 pp 659-673 february 2021
-[A 40] H Bounaceur, A Khenchaf, J-M Le Caillec “Analysis of Small Sea-Surface Targets Detection Performance According to Airborne Radar Parameters in Abnormal Weather Environments” MDPI Sensors 2022, 22(9), pp 3263
Financial time series analysis and portfolio management
1) Stochastic modelling and model calibration
-Designing an algorithm to calibrate (identify) the parameters of a nonlinear stochastic differential equation (Cox-Ingelson-Ross) from historical rate curves [A21-A22]
-Showing that nonlinearity in the risk factor model of Hedge Funds can be linked to the exchange rate model nonlinearity through nonlinearity hypothesis testing fusion [A24-A41]
2) Portfolio management
-Designing an algorithm for portfolio management based on technical indicator by fuzzy or non fuzzy approaches [A28].
-Designing an algorithm for portfolio management based Hidden Markov model for modeling the outperformance/underperformance [A 39].
-[A 21] S Dang Nguyen, J-M Le Caillec, A Hillion “Calibration of the Univariate Cox-Ingersoll-Ross Model and Parameters Selection through the Kullback-Leibler Divergence.” International Journal of Theoretical and Applied Finance , Vol 17, no 6, september 2014, pp 1450038
-[A 22] S Dang Nguyen, J-M Le Caillec, A Hillion “The deterministic shift extension and the affine dynamic Nelson-Siegel model” The North American Journal of Economics and Finance, Vol 29, july 2014, pp 402-417
-[A 24] A Itani J-M Le Caillec B Solaiman A Hamie “Probability-possibility hybrid systems for merging technical indices” Traitement du signal, Vol 31 no 3-4 september 2014 pp 401-419
-[A 28] J-M Le Caillec A Itani, D Gueriot, Y Rakotodratsimba “Stock Picking by Probability Possibility Approaches” ” IEEE Transactions on Fuzzy Systems, Vol 25, no 2, april 2017, pp 333- 349.
-[A 39] J-M Le Caillec, “Asset Picking Based on a Markov Chain Modeling the Asset Performance” IEEE Transactions Computational Intelligence vol 6 N° 1 pp 220-221 february 2022
-[A 41] J-M Le Caillec, “Hypothesis Testing Fusion for Nonlinearity Detection in Hedge Fund Price Returns” MDPI algorithms 2022, 15(8), pp 260