MA7165D STATISTICAL DIGITAL SIGNAL PROCESSING 
                   
                   
                  Pre-requisites: Nil                                                                                           
                                                                                                                   L    T    P     C 
                                                                                                                   3    1     0    3 
                  Total hours: 39 
                   
                  Course Outcomes: 
                   
                  CO1 Students acquire knowledge about random processes and their classification. 
                  CO2: Learn and apply concepts of signal modelling. 
                  CO3: Understand basic results about Lattice filters and Wiener filtering. 
                  CO4: Learn about power spectrum estimation and application to real world problems. 
                   
                  Module 1: (10 hours) 
                   
                  Discrete-Time  Random  Processes:  Random  Variables,  Random  Processes,  Filtering  Random 
                  Processes, Spectral Factorization, Special Types of Random Processes. 
                   
                  Module 2: (12 hours) 
                   
                  Signal  Modeling:  The  Least  Squares  Method,  The  PadeApproximtion,  Prony’s  Method,  Finite  Data 
                  Records, Stochastic Models. 
                   
                  Module 3: (17 hours) 
                   
                  Lattice  Filters  and  Wiener  Filtering:  The  FIR  Lattice  Filter,  Split  Lattice  Filter,  IIR  Lattice  Filters, 
                  Stochastic Modeling, The FIR Wiener Filter, IIR Wiener Filter, Discrete Kalman Filter. 
                  Spectrum Estimation: Nonparametric Methods, Minimum Variance Spectrum Estimation, The Maximum 
                  Entropy  Method,  Parametric  Methods,  Frequency  Estimation,  Principal  Components  Spectrum 
                  Estimation. 
                   
                  References:  
                   
                      1.  M. H. Hayes; “Statistical Digital Signal Processing and Modeling”, John Wiley & Sons, 2004.  
                      2.  G. J. Miao and M. A. Clements; “Digital Signal Processing and Statistical Classification”, Artech 
                           House, London, 2002.  
                      3.  R. M. Gray and L. D. Davisson ; “An Introduction to Statistical Signal Processing”, Cambridge 
                           University Press, 2004. 
                            
                            
                            
                            
                            
                            
                                            MA7165D STATISTICAL DIGITAL SIGNAL PROCESSING 
                   
                   
                   
                  Pre-requisites: Nil                                                                                           
                                                                                                                   L    T    P     C 
                                                                                                                   3    1     0    3 
                  Total hours: 39 
                   
                   
                  Brief Syllabus: 
                   
                   Discrete-Time Random Processes, Filtering Random Processes, Spectral Factorization, Special Types 
                   of  Random  Processes,  Signal  Modeling,  The  Least  Squares  Method,  The  PadeApproximtion, 
                   Prony’sMethod,Stochastic  Models,  The  FIR  Lattice  Filter,  Split  Lattice  Filter,  IIR  Lattice  Filters, 
                   Stochastic  Modeling,  The  FIR  Wiener  Filter,  IIR  Wiener  Filter,  Discrete  Kalman  Filter,  Spectrum 
                   Estimation: Nonparametric Methods, Minimum Variance Spectrum Estimation, The Maximum Entropy 
                   Method, Parametric Methods, Frequency Estimation, Principal Components Spectrum Estimation