EE 602A : Statistical Signal Processing

Instructor : Prof. R M Hegde, Dept of EE IIT Kanpur

Welcome to the course web page of EE 602 @ IIT Kanpur

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.

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

Text Books

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Reference Books

1. Statistical Signal Processing (Paperback) by Louis Scharf

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