E1 244: Detection and Estimation Theory (3:0)  January 2024
Instructor
Vaibhav Katewa
Assistant Professor
Robert Bosch Center for CyberPhysical Systems (RBCCPS) and Department of Electrical Communication Engineering (ECE)
Email: vkatewa(at)iisc(dot)ac(dot)in
Class Timings
TueThu, 2:003:30 PM. First class on January 02, 2024.
Venue
MP 20 (ECE Dept.)
Class Logistics
Registered students will be added to a Class group in Microsoft Teams. The link to join the Teams group is here. All the course correspondence will happen in this Teams group.
Teaching Assistants
Sai Pradeep Muppaneni
Niladri Halder
Souradeep Ghosh
Harit Goel
Aditya C
Soni Kumari
Course Overview and Syllabus
This is a graduate level course on statistical inference that deals with decision making based on observed data. The course is divided into two parts  Detection Theory and Estimation Theory. Detection theory provides a framework to make an intelligent guess regarding which hypothesis is true among a given set of n>2 hypotheses, while Estimation Theory provides a framework to intelligently guess the value of an unknown parameter that can be random or deterministic. The students will learn to mathematically formulate appropriate detection and estimation problems, solve these problems to get good/best detectors and estimators, and analyze their performance. This is a mathoriented course and will use concepts from probability and linear algebra.
We will broadly cover the following topics:
Bayesian and MinMax Hypothesis Testing
NeymanPearson Hypothesis Testing
Multiple Hypothesis Testing
Composite Hypothesis Testing and Generalized Likelihood Ratio Test (GLRT)
Detection of random/deterministic signals in presence of noise
Sequential Hypothesis Testing
Bayesian Estimation
MMSE and ML Estimators
Minimum Variance and Best Linear Unbiased Estimators
CramerRao Bound and Consistency
Kalman Filter
Prerequisites
A graduate level course on probability. Contact me separately if you haven't taken such a course and still want to credit E1 244.
A fair level of understanding of Linear Algebra/Matrix Theory concepts.
Grading
Quizzes: 10%
Homeworks: 25%
One Midterm Exam: 25%
Final Exam: 40%
References
There is no required textbook for the course. Below is a list of useful reference books:
Fundamentals of Statistical Signal Processing  Volume I: Estimation Theory by Steven M. Kay. Prentice Hall, 1993.
Fundamentals of Statistical Signal Processing  Volume II: Detection Theory by Steven M. Kay. Prentice Hall, 1993.
Statistical Inference for Engineers and Data Scientists by Moulin and Veeravalli. Cambridge University Press, 2019.
An Introduction to Signal Detection and Estimation (2nd Edition) by H. Vincent Poor, SpringerVerlag, 1994.
Statistical Inference (2nd Edition) by Casella and Berger. Duxbury Press, 2002.
Statistical Signal Processing by Louis L. Scharf. Pearson India, 2010.
