Course Title: Statistical Signal and Array Processing
ECTS points: 6 ECTS
Number of classes per week:3+0+0+3
Lecturer: Prof. d-r Venceslav Kafedziski
Course Goals (acquired competencies): Upon finishing the course, it is expected that the student will understand and know how to implement the methods and the algorithms of statistical signal processing: estimation of parameters, random parameters and random processes, adaptive signal processing and array processing, know how to apply these methods and algorithms to real world problems, and be prepared to perform scientific work in the area of statistical signal processing.
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|