Exponential signals occur in extremely diverse applications and estimation of their parameters is one of the basic problems in applied sciences. Nevertheless there are only a handful of methods for exponential analysis that are recommended in the literature, and even those methods have relatively mediocre performance in more difficult scenarios. In this paper we attempt to correct this situation by making use of a system identification approach. The proposed methodology, which we call EASI (Exponential Analysis via System Identification), is shown to have a satisfactory performance (i.e., high resolution and small statistical variability) for practical data lengths, and this not only for white measurement noise but also in cases with highly...
Low-frequency oscillations in power systems can be modeled as an exponentially damped sinusoid (EDS)...
The exponential auto-regression model is a discrete analog of the second-order nonlinear differentia...
This paper demonstrates the effectiveness and versatility of an iterative deconvolution algorithm in...
An efficient computational algorithm is proposed for estimating the parameters of undamped exponenti...
This paper considers parameter estimation of superimposed exponential signals in multiplicative and ...
In this paper a system identification method is described for the case of measurement errors on inpu...
The problem of identification of autoregressive (AR) signals with noisy measurements is considered. ...
System identification under noisy environment has axiomatic importance in numerous fields, such as c...
A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals...
A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals...
This paper demonstrates the effectiveness and versatility of an iterative deconvolution algorithm in...
Email Print Request Permissions The use of first- and second-order information in the characterizati...
Methods to estimate the parameters of a noisy signal are discussed. The signal typically consists of...
An important hurdle in multi-exponential analysis is the correct detection of the number of componen...
A new identification problem of estimating parameters of linear dynamic systems from random threshol...
Low-frequency oscillations in power systems can be modeled as an exponentially damped sinusoid (EDS)...
The exponential auto-regression model is a discrete analog of the second-order nonlinear differentia...
This paper demonstrates the effectiveness and versatility of an iterative deconvolution algorithm in...
An efficient computational algorithm is proposed for estimating the parameters of undamped exponenti...
This paper considers parameter estimation of superimposed exponential signals in multiplicative and ...
In this paper a system identification method is described for the case of measurement errors on inpu...
The problem of identification of autoregressive (AR) signals with noisy measurements is considered. ...
System identification under noisy environment has axiomatic importance in numerous fields, such as c...
A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals...
A simple algorithm is developed for unbiased parameter identification of autoregressive (AR) signals...
This paper demonstrates the effectiveness and versatility of an iterative deconvolution algorithm in...
Email Print Request Permissions The use of first- and second-order information in the characterizati...
Methods to estimate the parameters of a noisy signal are discussed. The signal typically consists of...
An important hurdle in multi-exponential analysis is the correct detection of the number of componen...
A new identification problem of estimating parameters of linear dynamic systems from random threshol...
Low-frequency oscillations in power systems can be modeled as an exponentially damped sinusoid (EDS)...
The exponential auto-regression model is a discrete analog of the second-order nonlinear differentia...
This paper demonstrates the effectiveness and versatility of an iterative deconvolution algorithm in...