Course details
Code
CS-570
Name
Statistical Signal Processing
Program
Postgraduate
Areas
Computer Networks and Telecommunications
Computational and Cognitive Vision and Robotics
Multimedia Technology
Description
This course focuses on problems, algorithms, and solutions for processing signals in a manner that is
responsive to a changing environment. Adaptive signal processing systems are developed which take advantage of the statistical properties of the received signals. The course analyzes the performance of adaptive filters and considers the application of the theory to a variety
of practical problems such as interference and echo cancellation, signal and system identification, and channel equalization. The class is designed as an advanced statistical signal processing course in which students will build a strong foundation in approaching problems in
such diverse areas as acoustic, sonar, radar, geophysical, biomedical, and communications signal processing. Understanding of the theoretical foundations of statistical and adaptive signal processing theory will be achieved through a combination of theoretical and
computer-based homework assignments. Course Outline: PART I: BACKGROUND MATERIAL AND LINEAR OPTIMUM FILTERING Topic 1: Background MaterialAdaptive filtering: Concepts and applicationsDiscrete-time signal processingStationary processes and
modelsSpectrum analysisLinear algebra: Eigenanalysis and matrix decompositions Topic 2: Wiener FilteringMinimum mean square error (MMSE) and the orthogonality principleDigital Wiener filter and Wiener-Hopf equationsConstrained linear MMSE
estimationApplications: Minimum variance beamforming Topic 3: Linear PredictionForward and backward predictionLevinson-Durbin algorithmLattice filtersApplications: DPCM speech coding PART II: ADAPTIVE FILTERING METHODS Topic 4: Stochastic
MethodsSteepest Descent algorithmLeast-Mean-Square (LMS) algorithmProperties of the LMSNormalized and frequency-domain LMSGradient adaptive lattice methodsRecursive LMS (RLMS) for adaptive IIR filteringApplications: Active noise control and echo-cancellation
Topic 5: Least Squares MethodsLeast squares and orthogonalityRecursive least squares adaptive algorithmsProperties of RLSApplications: ADPCM speech encoding
ECTS
6
Prerequisites
CS-370, CS-
217
Course website
Course email
hy570 AT csd DOT uoc DOt grShow email