Course Title and Code

Detection and Estimation Theory (EE5604)

Programme

PG, UG

Course Credit

3-0-0-3 (Lecture-Tutorial-Practical-Total Credits)

Course Category

Elective

Target Discipline

EE, CS

Prerequisite

MA2040 Probability, Stochastic Processes, and Statistics or equivalent course on probability

Course Content

S/N Topic Lecture (hours)
1 Introduction, Review of relevant concepts from Probability and Linear Algebra 3
2 Detection Theory: Hypothesis testing – Bayesian, Minimax, and Neyman-Pearson, Multiple hypothesis testing, Composite hypothesis testing, and generalized likelihood ratio test (GLRT) 10
3 Detection of deterministic and random signals in noise, Sequential detection – Sequential probability ratio test 7
4 Estimation Theory: Unbiasedness, Consistency, Minimum variance unbiased estimation, Cramer-Rao lower bound 6
5 Sufficient statistics, Rao-Blackwell theorem, Best linear unbiased estimation, Maximum likelihood estimation (MLE), Asymptotic performance of MLEs 6
6 Bayesian estimation - MMSE and MAP estimators, Linear MMSE estimation, Kalman filter 10

Learning Outcomes

  • Gain the ability to use mathematical tools to draw inferences from imperfect or incomplete measurements.
  • Learn to formulate appropriate detection and estimation problems and solve these problems to get good detectors and estimators.
  • Understand the performance limits of different detection and estimation techniques.

Text/Reference Books

  • H. Vincent Poor. An Introduction to Signal Detection and Estimation (2nd Ed.), Springer-Verlag New York, Inc., 1994, ISBN-13: 978-0387941738
  • George Casella and Roger L. Berger. Statistical Inference, Duxbury Press, Pacific Grove, PA, second edition, 2002, ISBN-13: 978-8131503942
  • S.M. Kay, Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory, Prentice Hall, 1993, ISBN-13: 978-0133457117
  • S.M. Kay, Fundamentals of Statistical Signal Processing, Volume II: Detection Theory, Prentice Hall, 1993, ISBN-13: 978-0135041352