Modélisation de signaux longs multicomposantes modulés non linéairement en fréquence et en amplitude Suivi de ces composantes dans le plan temps-fréquence. (Modeling of long-time multicomponent signals with nonlinear frequency and amplitude modulations - Component tracking in the time-frequency plan

Cette these propose une nouvelle methode pour modeliser les fonctions non lineaires de modulations d’amplitude et de frequence de signaux multicomposantes non stationnaires de duree longue. La methode repose sur une decomposition du signal en segments courts pour une modelisation locale sur les segments. Pour initialiser la modelisation, nous avons concu une premiere etape qui peut etre consideree comme un estimateur independant et non parametrique des fonctions de modulations. L’originalite de l’approche reside dans la definition d’une matrice de convergence totale integrant simultanement les valeurs d’amplitude et de frequence et utilise pour l’association d’un pic a une composante selon un critere d’acceptation stochastique. Suite a cette initialisation, la methode estime les fonctions de modulations par l'enchainement des etapes de segmentation, modelisation et fusion. Les fonctions de modulations estimees localement par maximum de vraisemblance sont connectees dans l'etape de fusion, qui supprime les discontinuites, et produit l’estimation globale sur la duree totale du signal. Les etapes sont concues afin de pouvoir modeliser des signaux multicomposantes avec des morts et naissances, ce qui en fait une de ses originalites par rapport aux techniques existantes. Les resultats sur des signaux reels et simules ont illustre les bonnes performances et l’adaptabilite de la methode proposee.

[1]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[2]  Christophe Ris,et al.  Assessing local noise level estimation methods: Application to noise robust ASR , 2000, Speech Commun..

[3]  Petar M. Djuric,et al.  Estimation of chirp signals by MCMC , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[4]  Abdel-Ouahab Boudraa,et al.  Nonstationary signals analysis by Teager-Huang Transform (THT) , 2006, 2006 14th European Signal Processing Conference.

[5]  Eric A. Wan,et al.  TWO-PASS QUANTILE BASED NOISE SPECTRUM ESTIMATION , 2002 .

[6]  Sylvain Meignen,et al.  A New Algorithm for Multicomponent Signals Analysis Based on SynchroSqueezing: With an Application to Signal Sampling and Denoising , 2012, IEEE Transactions on Signal Processing.

[7]  Anna Scaglione,et al.  Product high-order ambiguity function for multicomponent polynomial-phase signal modeling , 1998, IEEE Trans. Signal Process..

[8]  B. Friedlander,et al.  Multicomponent signal analysis using the polynomial-phase transform , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Ananthram Swami,et al.  On polynomial phase signals with time-varying amplitudes , 1996, IEEE Trans. Signal Process..

[11]  Jose A. Antonino-Daviu,et al.  Diagnosis of Induction Motor Faults in Time-Varying Conditions Using the Polynomial-Phase Transform of the Current , 2011, IEEE Transactions on Industrial Electronics.

[12]  Ljubisa Stankovic,et al.  Local polynomial Wigner distribution , 1997, Signal Process..

[13]  Mostefa Mesbah,et al.  IF estimation for multicomponent signals using image processing techniques in the time-frequency domain , 2007, Signal Process..

[14]  I. Djurovic,et al.  Recent advances in the estimation of the polynomial-phase signals , 2012, 2012 Mediterranean Conference on Embedded Computing (MECO).

[15]  Li Deng,et al.  Recursive estimation of nonstationary noise using iterative stochastic approximation for robust speech recognition , 2003, IEEE Trans. Speech Audio Process..

[16]  I. Daubechies,et al.  Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool , 2011 .

[17]  Xiang-Gen Xia,et al.  Discrete chirp-Fourier transform and its application to chirp rate estimation , 2000, IEEE Trans. Signal Process..

[18]  Robert Boorstyn,et al.  Single tone parameter estimation from discrete-time observations , 1974, IEEE Trans. Inf. Theory.

[19]  Hans-Peter Kriegel,et al.  Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.

[20]  Benjamin Friedlander,et al.  The discrete polynomial-phase transform , 1995, IEEE Trans. Signal Process..

[21]  Jianyu Yang,et al.  Multicomponent chirp signals analysis using product cubic phase function , 2006, Digit. Signal Process..

[22]  M. Jabloun,et al.  Modélisation de signaux fortement non stationnaires à phase et à amplitude locales polynomiales , 2007 .

[23]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[24]  Jakob Ängeby,et al.  Estimating signal parameters using the nonlinear instantaneous least squares approach , 2000, IEEE Trans. Signal Process..

[25]  S. Nakamura,et al.  Sequential Noise Compensation by Sequential Monte Carlo Method , 2001, NIPS.

[26]  Abdelhak M. Zoubir,et al.  Analysis of Multicomponent Polynomial Phase Signals , 2007, IEEE Transactions on Signal Processing.

[27]  P. D. Moral,et al.  Méthodes de Monte Carlo par Chaînes de Markov (MCMC) , 2014 .

[28]  LJubisa Stankovic,et al.  Instantaneous frequency estimation using the Wigner distribution with varying and data-driven window length , 1998, IEEE Trans. Signal Process..

[29]  Igor Djurovic,et al.  Are genetic algorithms useful for the parameter estimation of FM signals? , 2012, Digit. Signal Process..

[30]  Joseph M. Francos,et al.  Bounds for estimation of multicomponent signals with random amplitude and deterministic phase , 1995, IEEE Trans. Signal Process..

[31]  Peter O'Shea,et al.  A fast algorithm for estimating the parameters of a quadratic FM signal , 2004, IEEE Transactions on Signal Processing.

[32]  Meryem Jabloun,et al.  A New Flexible Approach to Estimate the IA and IF of Nonstationary Signals of Long-Time Duration , 2007, IEEE Transactions on Signal Processing.

[33]  P. Loughlin,et al.  On the amplitude‐ and frequency‐modulation decomposition of signals , 1996 .

[34]  Leon Cohen,et al.  On an ambiguity in the definition of the amplitude and phase of a signal , 1999, Signal Process..

[35]  Petros Maragos,et al.  On amplitude and frequency demodulation using energy operators , 1993, IEEE Trans. Signal Process..

[36]  T. Abatzoglou,et al.  "Fast maximum likelihood joint estimation of frequency and frequency rate" , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[37]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[38]  Meir Feder,et al.  Recursive expectation-maximization (EM) algorithms for time-varying parameters with applications to multiple target tracking , 1999, IEEE Trans. Signal Process..

[39]  T. Claasen,et al.  THE WIGNER DISTRIBUTION - A TOOL FOR TIME-FREQUENCY SIGNAL ANALYSIS , 1980 .

[40]  Benjamin Friedlander,et al.  The achievable accuracy in estimating the instantaneous phase and frequency of a constant amplitude signal , 1993, IEEE Trans. Signal Process..

[41]  I. Cohen,et al.  Noise estimation by minima controlled recursive averaging for robust speech enhancement , 2002, IEEE Signal Processing Letters.

[42]  Bernard C. Picinbono,et al.  On instantaneous amplitude and phase of signals , 1997, IEEE Trans. Signal Process..

[43]  Petar M. Djuric,et al.  Parameter estimation of chirp signals , 1990, IEEE Trans. Acoust. Speech Signal Process..

[44]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[45]  Jun Zhu,et al.  MAXIMUM LIKELIHOOD ESTIMATION OF CO-CHANNEL MULTICOMPONENT POLYNOMIAL PHASE SIGNALS USING IMPORTANCE SAMPLING , 2011 .

[46]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[47]  Mark R. Morelande,et al.  On the performance of cyclic moments-based parameter estimators of amplitude modulated polynomial phase signals , 2002, IEEE Trans. Signal Process..

[48]  Yingbo Hua,et al.  Fast Quadratic Phase Transform for Estimating the Parameters of Multicomponent Chirp Signals, , 1997, Digit. Signal Process..

[49]  Norden E. Huang,et al.  On Instantaneous Frequency , 2009, Adv. Data Sci. Adapt. Anal..

[50]  Yuhua Dong,et al.  Analysis of a New Joint Time-Frequency Distribution of Suppressing Cross-Term , 2012 .

[51]  Rainer Martin,et al.  Noise power spectral density estimation based on optimal smoothing and minimum statistics , 2001, IEEE Trans. Speech Audio Process..

[52]  Thomas F. Quatieri,et al.  Speech analysis/Synthesis based on a sinusoidal representation , 1986, IEEE Trans. Acoust. Speech Signal Process..

[53]  David Vakman,et al.  On the analytic signal, the Teager-Kaiser energy algorithm, and other methods for defining amplitude and frequency , 1996, IEEE Trans. Signal Process..

[54]  Vladimir Katkovnik Nonparametric estimation of instantaneous frequency , 1997, IEEE Trans. Inf. Theory.

[55]  Philipos C. Loizou,et al.  A noise-estimation algorithm for highly non-stationary environments , 2006, Speech Commun..

[56]  Boualem Boashash,et al.  Adaptive instantaneous frequency estimation of multicomponent FM signals using quadratic time-frequency distributions , 2002, IEEE Trans. Signal Process..

[57]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[58]  Alexander Fischer,et al.  Quantile based noise estimation for spectral subtraction and Wiener filtering , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[59]  Boaz Porat,et al.  Estimation and classification of polynomial-phase signals , 1991, IEEE Trans. Inf. Theory.

[60]  K. Coughlin,et al.  11-Year solar cycle in the stratosphere extracted by the empirical mode decomposition method , 2004 .

[61]  B. Friedlander Parametric signal analysis using the polynomial phase transform , 1993, [1993 Proceedings] IEEE Signal Processing Workshop on Higher-Order Statistics.

[62]  Steven M. Kay,et al.  Mean likelihood frequency estimation , 2000, IEEE Trans. Signal Process..

[63]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[64]  Meryem Jabloun,et al.  Estimation of the Amplitude and the Frequency of Nonstationary Short-time Signals , 2008 .

[65]  Steven M. Kay,et al.  Maximum likelihood parameter estimation of superimposed chirps using Monte Carlo importance sampling , 2002, IEEE Trans. Signal Process..

[66]  Francis Castanié,et al.  Digital Spectral Analysis: parametric, non-parametric and advanced methods , 2011 .

[67]  C. H. Greenewalt Bird song: acoustics and physiology , 1968 .

[68]  Sergio Barbarossa,et al.  Detection and estimation of the instantaneous frequency of polynomial-phase signals by multilinear time-frequency representations , 1993, [1993 Proceedings] IEEE Signal Processing Workshop on Higher-Order Statistics.