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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.


  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.