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Statistical signal and array processing

Course: Statistical signal and array processing

Code: ФЕИТ10026

ECTS points: 6 ECTS

Number of classes per week: 3+0+0+3

Lecturer: prof. d-r Venceslav Kafedziski

Subject of the course content: Random vectors: definition, moments, characteristic functions, multi-dimensional Gaussian distribution. Discrete random processes: definition, stationarity and ergodicity, autocorrelation and power spectral density. Parameter estimation: MVUE, ML, LS. Random parameter estimation: MAP, MMSE, and the orthogonality principle. Optimal estimation of discrete random processes: Wiener and Kalman filters. Parametric models of discrete random processes: AR, MA and ARMA. Spectral analysis of discrete random processes: peiodogram, correlogram, methods using the parametric models, high resolution methods. Adaptive signal processing: the method of steepest descent, LMS and RLS algorithms. Array signal processing: beamforming, optimal and adaptive processing, high resolution methods. Sensor array signal processing. Compressive sampling (compressed sensing) and dimensionality reduction. Applications of the described methods and algorithms.

Literature:

  1. D. G. Manolakis, V. K. Ingle, S. M. Kogon, “Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing”, Artech House, 2005.
  2. S. Haykin, K. J. Ray Liu, “Handbook on Array Processing and Sensor Networks”, Wiley-IEEE, 2009.
  3. R. Baraniuk, M. A. Davenport,  M. F. Duarte, C. Hegde, “An Introduction to Compressive Sensing”, CONNEXIONS, 2012.