RELAX-based estimation of damped sinusoidal signal parameters

Abstract This paper presents an extended RELAX algorithm that can be used to estimate the parameters of exponentially damped sinusoidal signals. The extended RELAX-algorithm introduced herein minimizes a nonlinear least squares criterion and is a maximum likelihood estimator when the noise is a zero-mean white Gaussian random process. Simulation results show that the performance of the extended RELAX algorithm can be better than that of the best available singular value decomposition based algorithm, especially when the noise is unknown and colored or when the number of data samples is large.

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