VL4011 Adaptive Signal Processing Syllabus:
VL4011 Adaptive Signal Processing Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
to understand the basic principles of discrete random signal processing
to understand the principles of spectral estimation
to learn about the weiner and adaptive filters
to understand the different signal detection and estimation methods
to acquire skills to design synchronization methods for proper functioning of the system
UNIT I DISCRETE RANDOM SIGNAL PROCESSING
Discrete Random Processes, Random Variables, Parseval’s Theorem, Wiener-Khintchine Relation, Power Spectral Density, Spectral Factorization, Filtering Random Processes, Special Types of Random Processes
UNIT II SPECTRAL ESTIMATION
Introduction, Nonparametric Methods – Periodogram, Modified Periodogram, Bartlett, Welch and Blackman-Tukey Methods, Parametric Methods – ARMA, AR and MA Model Based Spectral Estimation, Solution Using Levinson-Durbin Algorithm.
UNIT III WEINER AND ADAPTIVE FILTERS
Weiner Filter: FIR Wiener Filter, IIR Wiener Filter, Adaptive Filter: FIR Adaptive Filters – Steepest Descent Method- LMS Algorithm, RLS Adaptive Algorithm, Applications.
UNIT IV DETECTION AND ESTIMATION
Bayes Detection Techniques, Map, Ml,– Detection of M-Ary Signals, Neymanpearson, Minimax Decision Criteria. Kalman Filter- Discrete Kalman Filter, The Extended Kalman Filter, Application.
UNIT V SYNCHRONIZATION
Signal Parameter Estimation, Carrier Phase Estimation, Symbol Timing Estimator, Joint Estimation of Carrier Phase and Symbol Timing.
TOTAL: 45 PERIODS
PRACTICAL EXERCISES: 30 PERIODS
1. Design of Non- Parametric and Parametric for Spectral Estimation
2. Design of Linear Prediction Filter Using Matlab
3. Design of LMS Filter Using Matlab
4. Design of RLS Filter Using Matlab
5. Design of Extended Kalman Filter Using Matlab
COURSE OUTCOMES:
On successful completion of this course, students will be able to
CO1:Analyze the basic principles of discrete random signal processing
CO2:Analyze the principles of spectral estimation
CO3:Analyze the Weiner and Adaptive filters
CO4:Analyze the different signal detection and estimation methods
CO5:Design the synchronization methods for proper functioning of the system
REFERENCES
1. Monson H. Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley and Sons, Inc, Singapore, 2009.
2. John G. Proakis., “Digital Communication”, 4th Edition, McGraw Hill Publications, 2001.
3. Simon Haykin, “Adaptive Filter Theory”, Pearson Education, Fourth Edition, 2003
4. Bernard Sklar and Pabitra Kumar Roy, “Digital Communications: Fundamentals and Applications”, 2/E, Pearson Education India, 2009
5. Paulo S. R. Diniz, “Adaptive Filtering Algorithms and Practical Implementation”, Springer, 2011