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Abstract

This paper presents an approach to improve Prony’s method of identifying a linear time-invariant system. The method is based on a nonlinear transformation of parameters, which leads to data averaging. The method yields better results than a direct application of the least squares approach to Prony’s method. A numerical example is given to demonstrate the improvement attained by the new algorithm. Signals are assumed to be contaminated by zero-mean Gaussian white noise.

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