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On a Detection Estimator Related to Entropy

  • R. N. Madan
Part of the Fundamental Theories of Physics book series (FTPH, volume 31-32)

Abstract

The paper discusses the problem of estimation of parameters in a Rayleigh distribution modified to take into account the additional information. Madan and Guild [1981] have already given the maximum likelihood estimator (MLE) and the minimum mean squared estimator (MMSE) for the problem. Here we propose a new type of estimator called the entropy estimator for finding the mean of the samples from a small number of observations. The entropy estimator is the ratio of the arithmetic mean to the geometric mean multiplied by a normalizing constant. After normalizing the three estimates appropriately, the tightness of the entropy estimator is demonstrated numerically.

Keywords

False Alarm Random Noise Rayleigh Distribution Linear Detector Entropy Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Abromowitz, A. and Stegun, I.A. (1965). Handbook of Mathematical Functions, pp. 255–293, Dover Publications, Inc., New York.Google Scholar
  2. Gray, A.H. and Markel, J.D. (1974). “A Spectral-Flatness Measure for Studying the Autocorrelation Method of Linear Prediction of Speech Analysis,” IEEE Transactions on Acoustics, Speech and Signal Processing, ASSP-22, No.3, pp. 207–217.Google Scholar
  3. Madan, R.N. and Guild, J. (1981). “Maximum Likelihood Estimation in Radar Signals,” International Symposium on Information Theory, IEEE, February 9–12, 1981, Santa Monica, CA.Google Scholar

Copyright information

© Kluwer Academic Publishers 1988

Authors and Affiliations

  • R. N. Madan
    • 1
  1. 1.Office of Naval ResearchArlingtonUSA

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