Electronic engineering | University of Parma

Professor
Academic discipline: Telecomunicazioni (ING-INF/03)
Field: A scelta dello studente
Type of training activity: Student's choice

72 hours
of face-to-face activities

9 credits

hub:

course unit
in - - -

lectures timetable unavailable

exam calls

Learning objectives

To describe the foundation of detection and estimation theory.

Prerequisites

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Course unit content

Discrete representation of deterministic and random signals.

Estimation theory--Statistic model for estimation. Estimation of deterministic parameters: ML criterion. Estimation of stochastic parameters: Bayes criterion. Cramer-Rao inequality. Minimum mean square linear estimation. Prediction and filtering. Wiener filter. Kalman filter.

Detection theory--Statistic model for detection. Bayes criterion, MAP criterion, Minimax criterion, Neyman-Pearson criterion. Detection in the presence of additive white Gaussian noise. Sufficient statistics. Matched filter. Detection in the presence of additive Gaussian colored noise: reversibility theorem. Detection in the presence of random parameters.

Full programme

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Bibliography

R. Raheli, G. Colavolpe, Trasmissione numerica, Monte Università Parma editore, 2004. H. L. Van Trees, "Detection, estimation and modulation theory, Part I", John Wiley and Sons, 2001. S. M. Kay, "Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory", Prentice Hall, 1993. A. Papoulis, "Probability, random variables and stochastic processes", McGraw-Hill, 3rd ed., 1991. S. M. Kay, "Fundamentals of Statistical Signal Processing, Volume II: Detection Theory", Prentice Hall, 1993.

Teaching methods

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Assessment methods and criteria

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Other information

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DETECTION AND ESTIMATION THEORY cod. 16626

Learning objectives

Prerequisites

Course unit content

Full programme

Bibliography

Teaching methods

Assessment methods and criteria

Other information

DETECTION AND ESTIMATION THEORY
cod. 16626