EC863 Statistical Signal Processing

Course Name: 

EC863 Statistical Signal Processing

Programme: 

M.Tech(SPML)

Category: 

Elective (Ele)

Credits (L-T-P): 

(4-0-0) 4

Content: 

Introduction to Adaptive Filters: General properties, filtering, prediction and smoothing, Applications in Communications, Optimal Signal Processing, Principles of orthogonality, minimum square error, Wiener Hopf equations, state space model, innovations process, Kalman filter equations. Linear Adaptive Equalisation, Gradient search and steepest descent adaptation algorithms, Transient and Steady state properties including convergence rate and mis-adjustment, least square estimation, Recursive Least Squares (RLS) algorithms, Introduction to Fast Recursive Algorithms for Equalization, lattice filtering for RLS. Tracking time-varying systems

References: 

S.J. Orfanidis, Optimum Signal Processing, McGraw Hill, 1989.
S. Haykin, Adaptive FilterTheory, Pearson, 1996.
Mayson H. Hayes, Statistical Digital Signal Processing and Modeling, Wiley, 1996.

Department: 

Electronics and Communication Engineering(ECE)
 

Contact us

Dr. T. Laxminidhi,  Professor and Head, 
Department of E&C, NITK, Surathkal
P. O. Srinivasnagar,
Mangalore - 575 025 Karnataka, India.

Connect with us

We're on Social Networks. Follow us & get in touch.