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.
"Improvement of Prony's Method of System Identification via
Nonlinear Parameter Transformation,"
Journal of the Arkansas Academy of Science: Vol. 62
, Article 20.
Available at: https://scholarworks.uark.edu/jaas/vol62/iss1/20