COURSE SUMMARY
Course Title: 
Adaptive Signal Processing
Course Code: 
19CCE451
Year Taught: 
2019
Type: 
Elective
Degree: 
Undergraduate (UG)
School: 
School of Engineering
Campus: 
Chennai
Coimbatore

Adaptive Signal Processing is an elective course offered in the B. Tech. in Computer and Communication Engineering program at the School of Engineering, Amrita Vishwa Vidyapeetham.

Pre Requisite(s): Signal Processing

Objectives

  • To introduce the adaptive filter for estimation and tracking
  • To develop various adaptive algorithms for communication systems.
  • To apply the adaptive theory to a variety of practical problems

Course Outcomes

  • CO1: Able to analyze the filtering tasks and identify the need for adaptation in filtering
  • CO2: Able to design filter to meet performance requirements derived from various real life applications
  • CO3: Able to develop algorithms for the design of filters to track variations of non-stationary random process
  • CO4: Able to evaluate the performance of the developed filter in terms of computational complexity, convergence time and stability

CO – PO Mapping

PO/PSO/CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
CO1 3 2 2 - - - - - - - - - 2 -
CO2 2 2 3 - - - - - - - - - 2 -
CO3 2 3 3 2 - - - - - - - 2 3 2
CO4 3 2 3 2 - - - - - - - 2 3 2

Unit 1

Discrete time stochastic processes - Re-visiting probability and random variables - Discrete time random processes- Power spectral density – properties- Autocorrelation and covariance structures of discrete time random processes-Eigen-analysis of autocorrelation matrices - Spectrum Estimation - Non-parametric methods - Estimators and its performance analysis -periodogram estimators - signal modeling - parameter estimation using Yule- Walker Method.

Unit 2

LMS Algorithm - Need for adaptive filtering - Wiener FIR adaptive filters – Newton’s method - Steepest descent method –Convergence analysis - Performance surface – Least Mean Square (LMS) adaption algorithms– Convergence – Excess mean square error –Leaky LMS - Normalized LMS – Block LMS Least Squares Algorithm -Recursive least squares (RLS) algorithm for adaptive filtering of stationary process- Matrix inversion – Comparison with LMS – RLS for quasi-stationary signals- Exponentially weighted RLS- Sliding window RLS – RLS algorithm for array processing – Adaptive beam forming – Other applications of adaptive filters – Echo cancellation – Channel Equalization.

Unit 3 

Kalman Filtering - Statistical filtering for non-stationary signals – Kalman filtering- Principles – Initialization and tracking – Scalar and vector Kalman filter – Applications in signal processing – Time varying channel estimation – Radar tracking.

Textbook(s)

  • Simon O. Haykin, “Adaptive Filter Theory”, 5 th Edition, Pearson Education Limited, 2014.
  • Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon, “Statistical and Adaptive SignalProcessing: Spectral Estimation, Signal Modeling, Adaptive Filtering, and Array Processing”, McGraw-Hill, 2005.

Reference(s)

  • Monson H.Hayes, “Statistical Digital Signal Processing and Modeling”, John Wiley and Sons, Inc., Singapore, 2002.
  • Sopocles J. Orfanidis, “Optimum Signal Processing”, McGraw Hill, 2007.

Evaluation Pattern

Assessment Internal External
Periodical 1 (P1) 15 -
Periodical 2 (P2) 15 -
*Continuous Assessment (CA) 20 -
End Semester - 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.