Prony's method

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Prony analysis (Prony's method) was developed by Gaspard Riche de Prony in 1795. However, practical use of the method awaited the digital computer.[1] Similar to the Fourier transform, Prony's method extracts valuable information from a uniformly sampled signal and builds a series of damped complex exponentials or sinusoids. This allows for the estimation of frequency, amplitude, phase and damping components of a signal.

The method[edit]

Let f(t) be a signal consisting of N evenly spaced samples. Prony's method fits a function

\hat{f}(t) = \sum_{i=1}^{N} A_i e^{\sigma_i t} \cos(2\pi f_i t + \phi_i)

to the observed f(t). After some manipulation utilizing Euler's formula, the following result is obtained. This allows more direct computation of terms.

 \hat{f}(t) &= \sum_{i=1}^{N} A_i e^{\sigma_i t} \cos(2\pi f_i t + \phi_i) \\
            &= \sum_{i=1}^{N} \frac{1}{2} A_i e^{\pm j\phi_i}e^{\lambda_i t}


  • \lambda_i = \sigma_i \pm j \omega_i are the eigenvalues of the system,
  • \sigma_i are the damping components,
  • \phi_i are the phase components,
  • f_i are the frequency components,
  • A_i are the amplitude components of the series, and
  • j is the imaginary unit (j^2 = -1).


Prony's method is essentially a decomposition of a signal with M complex exponentials via the following process:

Regularly sample \hat{f}(t) so that the n-th of N samples may be written as

F_n = \hat{f}(\Delta_t n) = \sum_{m=1}^{M} \Beta_m e^{\lambda_m \Delta_t n}.

If \hat{f}(t) happens to consist of damped sinusoids, then there will be pairs of complex exponentials such that

   \Beta_a &= \frac{1}{2} A_i e^{ \phi_i j}, \\
   \Beta_b &= \frac{1}{2} A_i e^{-\phi_i j}, \\
 \lambda_a &= \sigma_i + j \omega_i, \\
 \lambda_b &= \sigma_i - j \omega_i,


 \Beta_a e^{\lambda_a t} + \Beta_b e^{\lambda_b t}
  &= \frac{1}{2} A_i e^{\phi_i j} e^{(\sigma_i + j \omega_i) t} +
     \frac{1}{2}A_i e^{-\phi_i j} e^{(\sigma_i - j \omega_i) t} \\
  &= A_i e^{\sigma_i t} \cos(\omega_i t + \phi_i).

Because the summation of complex exponentials is the homogeneous solution to a linear difference equation, the following difference equation will exist:

\hat{f}(\Delta_t n) = -\sum_{m=1}^{M} \hat{f}[\Delta_t (n - m)] P_m.

The key to Prony's Method is that the coefficients in the difference equation are related to the following polynomial:

 \sum_{m = 1}^{M + 1} P_m x^{m - 1} = \prod_{m=1}^{M} \left(x - e^{\lambda_m}\right).

These facts lead to the following three steps to Prony's Method:

1) Construct and solve the matrix equation for the P_m values:

 F_N \\
 \vdots \\
 F_{N-1} & \dots & F_{0} \\
 \vdots & \ddots & \vdots \\
 F_{2N-2} & \dots & F_{N-1}
 P_1 \\
 \vdots \\

Note that if N \ne M, a generalized matrix inverse may be needed to find the values P_m.

2) After finding the P_m values find the roots (numerically if necessary) of the polynomial

 x^M + \sum_{m = 1}^{M} P_m x^{m - 1}.

The m-th root of this polynomial will be equal to e^{\lambda_m}.

3) With the e^{\lambda_m} values the F_n values are part of a system of linear equations that may be used to solve for the \Beta_m values:

 F_{k_1} \\
 \vdots \\
 (e^{\lambda_1})^{k_1} & \dots & (e^{\lambda_M})^{k_1} \\
 \vdots & \ddots & \vdots \\
 (e^{\lambda_1})^{k_M} & \dots & (e^{\lambda_M})^{k_M}
 \Beta_1 \\
 \vdots \\

where M unique values k_i are used. It is possible to use a generalized matrix inverse if more than M samples are used.

Note that solving for \lambda_m will yield ambiguities, since only e^{\lambda_m} was solved for, and e^{\lambda_m} = e^{\lambda_m \,+\, q 2 \pi j} for an integer q. This leads to the same Nyquist sampling criteria that discrete Fourier transforms are subject to:

 \left|Im(\lambda_m)\right| = \left|\omega_m\right| < \frac{1}{2 \Delta_t}.




  1. ^ Hauer, J.F. et al. (1990). "Initial Results in Prony Analysis of Power System Response Signals". IEEE Transactions on Power Systems, 5, 1, 80-89.


  • Rob Carriere and Randolph L. Moses, "High Resolution Radar Target Modeling Using a Modified Prony Estimator", IEEE Trans. Antennas Propogat., vol.40, pp. 13–18, January 1992.