DocumentCode :
961961
Title :
A New Flexible Approach to Estimate the IA and IF of Nonstationary Signals of Long-Time Duration
Author :
Jabloun, Meryem ; Leonard, Francois ; Vieira, Michelle ; Martin, Nadine
Author_Institution :
INPG, Saint Martin d´´Heres
Volume :
55
Issue :
7
fYear :
2007
fDate :
7/1/2007 12:00:00 AM
Firstpage :
3633
Lastpage :
3644
Abstract :
In this paper, we propose an original strategy for estimating and reconstructing monocomponent signals having a high nonstationarity and long-time duration. We locally apply to short-time duration intervals the strategy developed in our previous work about nonstationary short-time signals. This paper describes a nonsequential time segmentation that provides segments whose lengths are suitable for modeling both the instantaneous amplitude and frequency locally with low-order polynomials. Parameter estimation is done independently for each segment by maximizing the likelihood function by means of the simulated annealing technique. The signal is then reconstructed by merging the estimated segments. The strategy proposed is sufficiently flexible for estimating a large variety of nonstationarity and specifically applicable to high-order polynomial phase signals. The estimation of a high-order model is not necessary. The error propagation phenomenon occurring with the known approach, the higher ambiguity function (HAF)-based method, is avoided. The proposed strategy is evaluated using Monte Carlo noise simulations and compared with the Cramer-Rao bounds (CRBs). The signal of a songbird is used as a real example of its applicability.
Keywords :
Monte Carlo methods; maximum likelihood estimation; signal reconstruction; simulated annealing; Cramer-Rao bounds; Monte Carlo noise simulations; error propagation phenomenon; high-order polynomial phase signals; higher ambiguity function-based method; low-order polynomials; maximum likelihood function; monocomponent signals; nonsequential time segmentation; nonstationary signals; parameter estimation; signal reconstruction; simulated annealing technique; Amplitude estimation; Frequency estimation; Gaussian noise; Image reconstruction; Maximum likelihood estimation; Monte Carlo methods; Parameter estimation; Phase estimation; Polynomials; Simulated annealing; Cramér–Rao bounds (CRBs); maximum likelihood; nonlinear modulation; nonstationary signal; polynomial phase signal; simulated annealing; time frequency (TF);
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2007.894254
Filename :
4244693
Link To Document :
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