M. Boizard, R. Boyer, G. Favier, and P. Comon, Performance estimation for tensor CP decomposition with structured factors, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015.
DOI : 10.1109/ICASSP.2015.7178618

J. H. De, M. Goulart, M. Boizard, R. Boyer, G. Favier et al., Statistical efficiency of structured CPD estimation applied to Wiener- Hammerstein modeling, Proc. European Signal Process. Conf. (EUSIPCO), pp.953-957
URL : https://hal.archives-ouvertes.fr/hal-01118725

T. Kolda and B. Bader, Tensor Decompositions and Applications, SIAM Review, vol.51, issue.3, pp.455-500, 2009.
DOI : 10.1137/07070111X

M. Mørup, Applications of tensor (multiway array) factorizations and decompositions in data mining, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.61, issue.1, pp.24-40, 2011.
DOI : 10.1002/widm.1

P. Comon, Tensors : A brief introduction, IEEE Signal Processing Magazine, vol.31, issue.3, pp.44-53, 2014.
DOI : 10.1109/MSP.2014.2298533
URL : https://hal.archives-ouvertes.fr/hal-00923279

A. Smilde, R. Bro, and P. Geladi, Multi-way analysis: applications in the chemical sciences, 2005.
DOI : 10.1002/0470012110

A. Cichocki, R. Zdunek, A. H. Phan, and S. Amari, Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation, 2009.
DOI : 10.1002/9780470747278

J. D. Carroll and J. Chang, Analysis of individual differences in multidimensional scaling via an n-way generalization of ???Eckart-Young??? decomposition, Psychometrika, vol.12, issue.3, pp.283-319, 1970.
DOI : 10.1007/BF02310791

R. A. Harshman, Foundations of the PARAFAC procedure: models and conditions for an " explanatory " multi-modal factor analysis, UCLA Working Papers in Phonetics, vol.16, pp.1-84, 1970.

C. E. Fernandes, G. Favier, and J. C. Mota, Blind channel identification algorithms based on the Parafac decomposition of cumulant tensors: The single and multiuser cases, Signal Processing, vol.88, issue.6, pp.1382-1401, 2008.
DOI : 10.1016/j.sigpro.2007.12.010
URL : https://hal.archives-ouvertes.fr/hal-00417610

G. Favier and A. Y. Kibangou, Tensor-based methods for system identification Part 2: Three examples of tensor-based system identification methods, Int. J. Sci. Tech. Automat. Control (IJ-STA), vol.3, issue.1, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00417555

D. Nion and N. D. Sidiropoulos, Tensor Algebra and Multidimensional Harmonic Retrieval in Signal Processing for MIMO Radar, IEEE Transactions on Signal Processing, vol.58, issue.11, pp.5693-5705, 2010.
DOI : 10.1109/TSP.2010.2058802

A. Y. Kibangou, G. Favier, and E. Glasgow, Non-iterative solution for PARAFAC with a Toeplitz matrix factor, pp.691-695, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00397808

M. Sorensen and P. Comon, Tensor decompositions with banded matrix factors, Linear Algebra and its Applications, vol.438, issue.2, pp.919-941, 2013.
DOI : 10.1016/j.laa.2011.10.044
URL : https://hal.archives-ouvertes.fr/hal-00740572

M. Sorensen and L. De-lathauwer, Blind Signal Separation via Tensor Decomposition With Vandermonde Factor: Canonical Polyadic Decomposition, IEEE Transactions on Signal Processing, vol.61, issue.22, pp.5507-5519, 2013.
DOI : 10.1109/TSP.2013.2276416

J. H. De, M. Goulart, and G. Favier, An algebraic solution for the Candecomp/PARAFAC decomposition with circulant factors, SIAM J. Matrix Anal. Applicat, vol.35, issue.4, pp.1543-1562, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00967263

X. Liu and N. D. Sidiropoulos, Cramér-Rao lower bounds for lowrank decomposition of multidimensional arrays, IEEE Trans. Signal Process, vol.49, issue.9, pp.2074-2086, 2001.

S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, 1993.

S. Sahnoun and P. Comon, Tensor polyadic decomposition for antenna array processing, Proc. Int. Conf, pp.233-240, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00986973

R. Boyer, Deterministic asymptotic Cram??r???Rao bound for the multidimensional harmonic model, Signal Processing, vol.88, issue.12, pp.2869-2877, 2008.
DOI : 10.1016/j.sigpro.2008.06.011

Z. Ben-haim and Y. C. Eldar, A Lower Bound on the Bayesian MSE Based on the Optimal Bias Function, IEEE Transactions on Information Theory, vol.55, issue.11, pp.5179-5196, 2009.
DOI : 10.1109/TIT.2009.2030451

H. L. Van-trees and K. L. Bell, Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking, 2007.
DOI : 10.1109/9780470544198

R. Haber and L. Keviczky, Nonlinear system identification ? Input- Output Modeling Approach, of Mathematical Modelling: Theory and Applications, 1999.
DOI : 10.1007/978-94-011-4481-0

L. L. Scharf, L. T. Mac, and . Worther, Geometry of the Cramer-Rao bound, Signal Processing, vol.31, issue.3, pp.301-311, 1993.
DOI : 10.1016/0165-1684(93)90088-R

P. Stoica and R. L. Moses, Spectral analysis of signals, 2005.

R. T. Behrens and L. L. Scharf, Signal processing applications of oblique projection operators, IEEE Transactions on Signal Processing, vol.42, issue.6, pp.1413-1424, 1994.
DOI : 10.1109/78.286957

K. B. Petersen and M. S. Pedersen, The matrix cookbook, 2012.

G. Tomasi and R. Bro, A comparison of algorithms for fitting the PARAFAC model, Computational Statistics & Data Analysis, vol.50, issue.7, pp.1700-1734, 2006.
DOI : 10.1016/j.csda.2004.11.013

R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, A Limited Memory Algorithm for Bound Constrained Optimization, SIAM Journal on Scientific Computing, vol.16, issue.5, pp.1190-1208, 1995.
DOI : 10.1137/0916069

A. Y. Kibangou and G. Favier, Wiener-Hammerstein systems modeling using diagonal Volterra kernels coefficients, IEEE Signal Processing Letters, vol.13, issue.6, pp.381-384, 2006.
DOI : 10.1109/LSP.2006.871705
URL : https://hal.archives-ouvertes.fr/hal-00417644

C. Tseng and E. J. Powers, Identification of cubic systems using higher order moments of i.i.d. signals, IEEE Transactions on Signal Processing, vol.43, issue.7, pp.1733-1735, 1995.
DOI : 10.1109/78.398739

G. Favier and A. L. De-almeida, Overview of constrained PARAFAC models, EURASIP Journal on Advances in Signal Processing, vol.2014, issue.1, pp.1-25, 2014.
DOI : 10.1186/1687-6180-2014-142
URL : https://hal.archives-ouvertes.fr/hal-01246040

J. Henrique-de and M. , respectively. He is currently a Ph.D student at Université Nice Sophia Antipolis (UNS), France, working at the I3S Laboratory His research interests include tensor models and decompositions, low-rank tensor recovery, tensor methods in signal processing and nonlinear system modeling and identification, Brazil, and the M.Sc. degree in Electronic Systems from Escola Politécnica da Universidade de São Paulo, 2006.

T. With-thomson and . Marconi, He later received the Habilitation to Lead Researches in 1995, from the University of Nice, France. He has been for nearly 13 years in industry, first with Crouzet-Sextant He spent 1987 with the ISL laboratory He joined in 1997 the Eurecom Institute He is research director with CNRS since 1998, first at laboratory I3S His research interests include Blind techniques, Statistical Signal and Array Processing, Tensor decompositions, Multi-Way Factor Analysis and its applications to biomedical end environment. Dr. Comon was Associate Editor of the IEEE Transactions on Signal Processing from 1995 to 1998, and a member of the French National Committee of Scientific Research from 1995 to 2000. He was the coordinator of the European Basic Research Working Group on High-Order Statistics, ATHOS, from, Pierre Comon (M'87 ? SM'95 ? F'07) received both the M.Sc. degree in 1982, and the Doctorate degree he acted as launching Associate Editor with the IEEE Transactions on Circuits and Systems I, in the area of Blind Techniques. Ha has also been a member of the editorial board of the Elsevier journal Signal Processing, and a member of the IEEE SPTM, BSP and SAM Technical Commitees. He is presently in the editorial board of SIAM Journal on Matrix Analysis and Applications, 1982.