Course Description
- This course will discuss statistical signal processing from both theoretical and practical perspectives.
- It will include the basics of Estimation and Detection Theory, Parametric, and Non-parametric spectrum estimation (including ML, MAP, LSE). The course will have a balanced focus on math formulations and practice. It will involve a Project/Term paper implementation and presentation to allow for more space to assimilate the concepts as well.
- Discussion Hour: Monday, 10:30-11:30
- Zoom Link for Discussion Hour given in the Announcement section.
- End Semester examination: 12/12/2020; 0900-1200 hrs
Course Content
- Structure of statistical reasoning, Introduction to Estimation theory
- Estimation: Minimum Variance Unbiased Estimator, Cramer Rao Lower Bound (CRLB) for scalar and vector parameters
- Estimation : Maximum Likeihood Estimation (MLE), Maximum Aposteriori Estimation (MAP), Linear Least Squares (LLSE) with examples of Gaussian mixture modeling (GMM) and Hidden Markov Modeling (HMM)
- Detection : Introduction, Neyman Pearson theoroem, Binary and Multiple hypothesis testing, Examples
- Any other parts of SSP that are relevant to the above course content
Course Audience
UG Students: BTech (3rd and 4th year) students
PG Students: All MTech and PhD students
Outcomes of this Course
On completion of this course, the student should be able to
- Understand the concepts of Estimation Theory
- Understand the concepts of Detection theory
- Able to design statistical signal processing systems
- Design Estimators for various signal processing and communication probelms
- Design Detectors for various signal processing problems
- Apply concepts learnt in this course for various applications like wireless communication, digital array processing, RADAR/SONAR signal processing, and related areas