E1 244-O Detection and Estimation 3:1 (January 2023)
Course Instructor: Sundeep Prabhakar Chepuri, ECE
Course description:The main goal of the course is to cover the two major domains of statistical inference, namely, estimation theory and detection theory, which include the many mathematical tools that engineers and statisticians use to draw inference from imperfect or incomplete measurements. The first part of the course develops statistical parameter estimation methods to extract information from data in noise. The second part of this course is about the application of statistical hypothesis testing to the detection of data in noise.
Syllabus
Minimum variance unbiased estimators, maximum likelihood theory, the Cramer-Rao bound, best linear unbiased estimators, Bayesian estimation techniques, the Wiener and Kalman filters, binary and multiple hypothesis testing, Neyman-Pearson detector, Bayes detector, compos- ite hypothesis testing, and sequential probability ratio test.
Textbooks / References
- Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory, S.M. Kay, Prentice Hall 1993, ISBN-13: 978-0133457117.
- Fundamentals of Statistical Signal Processing, Volume II: Detection Theory, S.M. Kay, Prentice 1993, ISBN-13: 978-0135041352.
- Statistical Signal Processing, L.L. Scharf, Pearson India, 2010, ISBN-13: 978-8131733615.
- An Introduction to Signal Detection and Estimation, H.V. Poor, Springer, 2nd edition, 1998, ISBN-13: 978-0387941738.
Prerequisites: Matrix theory and Random processes.
Grading:
- Homework assignments (3) 30%
- Midterm 20%
- Final Project 30%
- Final exam 20%.
Three assignments and the mid-term shall be treated as sessional assessment, while the final project and final examination shall be treated as the final assessment.