By Topic

Minimum Fisher information spectral analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
V. Zivojnovic ; Integrated Syst. for Signal Processing, Aachen Univ. of Technol., Germany ; D. Noll

Minimizing the Fisher information measure over the set of power spectrum densities fitting a finite number of autocorrelation lag constraints is treated. Due to an explicit control of the derivative values of the densities, the Fisher information measure produces a useful smoothing effect. The Fisher information based estimate exhibits improved characteristics compared to the maximum entropy approach proposed by Burg (1967). We show that the resulting power spectrum estimate is positive, and along with the autocorrelation constraints, satisfies a generalized Riccati differential equation. In general, the true estimate of the power spectrum may be obtained only by numerically integrating the corresponding boundary value problem. For real time applications, we therefore propose a fast and numerically stable approximate solution in explicit trigonometric form. Although suboptimal, the proposed approach has proven to be advantageous especially for flat spectra. The presented theory is verified on simulated examples

Published in:

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on  (Volume:5 )

Date of Conference:

21-24 Apr 1997