The sinusoidal parameter estimation problem is considered to fit a sum of damped sinusoids to a series of noisy observations. It is formulated as a nonlinear least-squares global optimization problem. A one-parametric case study is examined to determine an unknown frequency of a signal. Univariate Lipschitzbased deterministic methods are used for solving such problems within a limited computational budget. It is shown that the usage of local information in these methods (such as local tuning on the objective function behavior and/or evaluating the function first derivatives) can significantly accelerate the search for the problem solution with a required guarantee. Results of a numerical comparison with metaheuristic techniques frequently used in engineering design are also reported and commented on.
On the least-squares fitting of data by sinusoids
SERGEEV, Yaroslav;KVASOV, Dmitry;Mukhametzhanov M.
2016-01-01
Abstract
The sinusoidal parameter estimation problem is considered to fit a sum of damped sinusoids to a series of noisy observations. It is formulated as a nonlinear least-squares global optimization problem. A one-parametric case study is examined to determine an unknown frequency of a signal. Univariate Lipschitzbased deterministic methods are used for solving such problems within a limited computational budget. It is shown that the usage of local information in these methods (such as local tuning on the objective function behavior and/or evaluating the function first derivatives) can significantly accelerate the search for the problem solution with a required guarantee. Results of a numerical comparison with metaheuristic techniques frequently used in engineering design are also reported and commented on.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
