Statistical Signal Processing
September 2014
Course No.: 14ESP12
Instructor
Shahid M Shah, email id: FIRST NAME AT ece.iisc.ernet.in, FIRST NAME DOT nit AT gmail.com
Credits
4
Location
Room No. 422, Reva University
Lecture Hours
Tue: 8:30AM to 9:25AM
Wed: 11:40AM to 12:35PM
Thu: 9:25AM to 10:20AM, 11:40AM to 12:35PM
Announcement: 2nd Intenal on tuesday 11 November 2014, at 2:00 PM
RESULT of Internals is out. Click here to see the result
Ist internals will be held in Room No. 422, Time is 10:45AM to 12:15PM on thursday 30th October 2014. Sylabuss is whatever is covered till lecture No. 28
Course Syllabus
1-Random Processes:
Random variables, random processes, white noise, filtering random processes, spectral factorization, ARMA, AR and MA processes.
2-Signal Modeling:
Least squares method, Padé approximation, Prony's method, finite data records, stochastic models, Levinson- Durbin recursion; Schur recursion; Levinson recursion.
3-Spectrum Estimation:
Non-parametric methods, minimum-variance spectrum estimation, maximum entropy method, parametric methods, frequency estimation, principal components spectrum estimation.
4-Optimal and Adaptive Filtering:
FIR and IIR Wiener filters, Discrete Kalman filter, FIR Adaptive filters: Steepest descent, LMS, LMS- based algorithms, adaptive recursive filters, RLS algorithm.
5-Array Processing:
Array fundamentals, beam-forming, optimum array processing, performance considerations, adaptive beam- forming, linearly constrained minimum-variance beam-formers, side-lobe cancelers, space-time adaptive processing.
Prerequisites
Signals and Systems, Basic Probability Theory, Linear Algebra (Under Grad Level).
Course Grade
T.B.D
Homeworks
HW 1: Submit Assignment of Harmonic Process and Moving Average Process
HW2:
Dear students: Try solved examples in the book, and then after solving, look at the solution how the
author has solved the problem. Also solve the following problems from the book Monson H Hayes
1) 3.6
2) 3.8
3) 3.11
4) 3.12
5) 3.18
6) 3.25 part (a)
7) 3.26
8) 4.1
References
Schedule
Instructor
Shahid M Shah, email id: FIRST NAME AT ece.iisc.ernet.in, FIRST NAME DOT nit AT gmail.com
Credits
4
Location
Room No. 422, Reva University
Lecture Hours
Tue: 8:30AM to 9:25AM
Wed: 11:40AM to 12:35PM
Thu: 9:25AM to 10:20AM, 11:40AM to 12:35PM
Announcement: 2nd Intenal on tuesday 11 November 2014, at 2:00 PM
RESULT of Internals is out. Click here to see the result
Ist internals will be held in Room No. 422, Time is 10:45AM to 12:15PM on thursday 30th October 2014. Sylabuss is whatever is covered till lecture No. 28
Course Syllabus
1-Random Processes:
Random variables, random processes, white noise, filtering random processes, spectral factorization, ARMA, AR and MA processes.
2-Signal Modeling:
Least squares method, Padé approximation, Prony's method, finite data records, stochastic models, Levinson- Durbin recursion; Schur recursion; Levinson recursion.
3-Spectrum Estimation:
Non-parametric methods, minimum-variance spectrum estimation, maximum entropy method, parametric methods, frequency estimation, principal components spectrum estimation.
4-Optimal and Adaptive Filtering:
FIR and IIR Wiener filters, Discrete Kalman filter, FIR Adaptive filters: Steepest descent, LMS, LMS- based algorithms, adaptive recursive filters, RLS algorithm.
5-Array Processing:
Array fundamentals, beam-forming, optimum array processing, performance considerations, adaptive beam- forming, linearly constrained minimum-variance beam-formers, side-lobe cancelers, space-time adaptive processing.
Prerequisites
Signals and Systems, Basic Probability Theory, Linear Algebra (Under Grad Level).
Course Grade
T.B.D
Homeworks
HW 1: Submit Assignment of Harmonic Process and Moving Average Process
HW2:
Dear students: Try solved examples in the book, and then after solving, look at the solution how the
author has solved the problem. Also solve the following problems from the book Monson H Hayes
1) 3.6
2) 3.8
3) 3.11
4) 3.12
5) 3.18
6) 3.25 part (a)
7) 3.26
8) 4.1
References
- Monson H. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley & Sons (Asia) Pte. Ltd., 2002.
Download PDF version Part 1 Download PDF version Part 2 Download PDF version Part 3_ Download DVJU version - Dimitris G. Manolakis, Vinay K. Ingle, and Stephen M. Kogon, "Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing”, McGraw- Hill International Edition, 2000.
Schedule
- Lecture 1 (September 16). Revision of Fundementals of Signals and Systems.
- Lecture 2 (September 17). Revision of Fundementals of Signals and Systems, Revision of Probability Theory.
- Lecture 3 (September 18). Random Variables
- Lecture 4 (September 18). Random Variables
- Lecture 5 (September 23). Distribution Function, Density Function, Expectation
- Lecture 6 (September 24). Jointly Distributed RV, Joint moments.
- Lecture 7 (September 24). Linear Mean Square Estimation (LMS)
- Gaussian RV, Parameter Estimation
- Lecture 9 (September 30). Random Process Basics, Autocorrelation, Auto Covariance
- Lecture 10 (October 1). Random Process continued
- Lecture 11 (October 1). Mean Square convergence, Mean Ergodic Theorem
- October 2 Hiloday
- Lecture 12 (October 25). Revision and doubt clearing, Stationary Process, WSS Process, Gaussian Process
- Lecture 13 (October 27). Autocorrelation and Autocovariance Matrix and its properties
- Lecture 14 (October 28). White Noise, Power Spectral Density
- Lecture 15 (October 29). Filtering Random Process
- Lecture 16 (October 30). ist internal examination.
- Lecture 17 (November ). Spectral Factorization
- Lecture 18 (November ). Special RP: ARMA, AP, MA
- Lecture 19 (November ). Remaining of previous chapter
- Lecture 20 (November ). Signal Modelling: Least Square Method, Pade Approximation
- Lecture 21 (November ). Pade Approximation
- Lecture 22 (November ). Prony's Method
- Lecture 23 (November ). Prony's Method cont.
- Lecture 24 (November ). Shank's Method
- Lecture 21 (November )
- November ). Stochastic Models, Levinson-Durbin recursion