Estimation of the instantaneous amplitude and frequency of non-stationary short-time signals

  • Authors:

  • Meryem Jabloun;Nadine Martin;Francois Leonard;Michelle Vieira

  • Affiliations:

  • LIS-CNRS/INPG, 961 rue de la houille blanche, BP 46, 38402 Saint Martin d'Heres, France;LIS-CNRS/INPG, 961 rue de la houille blanche, BP 46, 38402 Saint Martin d'Heres, France;Institut de recherche d'Hydro-Quebec, 1800 Bd. Lionel Boulet, Varennes, Que., Canada J3X 1S1;LIS-CNRS/INPG, 961 rue de la houille blanche, BP 46, 38402 Saint Martin d'Heres, France

  • Venue:
  • Signal Processing
  • Year:
  • 2008

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Abstract

We consider the modeling of non-stationary discrete signals whose amplitude and frequency are assumed to be nonlinearly modulated over very short-time duration. We investigate the case where both instantaneous amplitude (IA) and instantaneous frequency (IF) can be approximated by orthonormal polynomials. Previous works dealing with polynomial approximations refer to orthonormal bases built from a discretization of continuous-time orthonormal polynomials. As this leads to a loss of the orthonormal property, we propose to use discrete orthonormal polynomial bases: the discrete orthonormal Legendre polynomials and a discrete base we have derived using Gram-Schmidt procedure. We show that in the context of short-time signals the use of these discrete bases leads to a significant improvement in the estimation accuracy. We manage the model parameter estimation by applying two approaches. The first is maximization of the likelihood function. This function being highly nonlinear, we propose to apply a stochastic optimization technique based on the simulated annealing algorithm. The problem can also be considered as a Bayesian estimation which leads us to apply another stochastic technique based on Monte Carlo Markov Chains. We propose to use a Metropolis Hastings (MH) algorithm. Both approaches need an algorithm parameter tuning that we discuss according our application context. Monte Carlo simulations show that the results obtained are close to the Cramer-Rao bounds we have derived. We show that the first approach is less biased than the second one. We also compared our results with the higher ambiguity function-based method. The methods proposed outperform this method at low signal to noise ratios (SNR) in terms of estimation accuracy and robustness. Both proposed approaches are of a great utility when scenarios in which signals having a small sample size are non-stationary at low SNRs. They provide accurate system descriptions which are achieved with only a reduced number of basis functions.