Course Details

Subject {L-T-P / C} : EE6333 : Estimation of Signals and Systems {3-0-0 / 3}
Subject Nature : Theory
Coordinator : Prof. Bidyadhar Subudhi


Introduction to probability theory and statistics: Probability, Random Variables, Stochastic Processes and noises System models and states: Continuous and discrete-time systems, Discretization, Propagation of states and covariance Least Square Estimation: Static state estimation, Recursive Least Squares, Wiener Filtering Kalman Filtering: Discrete-time Kalman Filter and its properties, Propagation of Covariance Sequential Kalman Filtering Information Filtering Square Root Filtering.

Course Objectives

  1. To learn about different estimation techniques such as LS, RLS, MLE, MAP and Kalman Filtering
    To apply the estimation techniques to different systems for parameter and state estimations

Course Outcomes

? CO1: Learn different parameter and state estimation techniques and filtering algorithms
? CO2: Apply the estimation and filtering algorithms to engineering problems
? CO3: Learn Methodologies on design and synthesis of optimal estimation algorithms
? CO4: Characterization of estimators and tools to study their performance
? CO4: To judge rationale for choosing a particular estimation/filtering technique

Essential Reading

  1. Dan Simon, Optimal State Estimation: Kalman, H_8, and Nonlinear Approaches, Wiley-Interscience, New Jersey, 2006
  2. M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, Wiley, 2008

Supplementary Reading

  1. 1. Athanasios Papoulis and S. Unnikrishna Pillai, Probability, Random Variables and Stochastic Processes, McGraw Hill, 2001
  2. Robert Brown and Patrick Hwang, Introduction to Random Signals and Applied Kalman Filtering, John Wiley, 1997