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EE602A: Statistical Signal Processing-I

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

  1. Structure of statistical reasoning, Introduction to Estimation theory
  2. Estimation: Minimum Variance Unbiased Estimator, Cramer Rao Lower Bound (CRLB) for scalar and vector parameters
  3. 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)
  4. Detection : Introduction, Neyman Pearson theoroem, Binary and Multiple hypothesis testing, Examples
  5. 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