Minimum Fisher information spectral analysis
作者： V. Zivojnovic , D. Noll
DOI: 10.1109/ICASSP.1997.604786
关键词:
摘要: Minimizing the Fisher information measure over set of power spectrum densities fitting a finite number autocorrelation lag constraints is treated. Due to an explicit control derivative values densities, produces useful smoothing effect. The based estimate exhibits improved characteristics compared maximum entropy approach proposed by Burg (1967). We show that resulting positive, and along with constraints, satisfies generalized Riccati differential equation. In general, true may be obtained only numerically integrating corresponding boundary value problem. For real time applications, we therefore propose fast stable approximate solution in trigonometric form. Although suboptimal, has proven advantageous especially for flat spectra. presented theory verified on simulated examples.
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