Instructor:

Dr. Saurabh Das (Course Coordinator)

Dr Abhirup Datta

Topics:

· Probability and random variables: a review; Mathematical description of random signals; Gauss-Markov Process;

· Linear dynamic systems with random inputs, steady-state analysis; state-space modeling, Cholesky decomposition;

· Basic concepts in estimation; Linear estimation in static systems

· Discrete Kalman filter basics; estimation for kinematic models; auto-correlated process noise; cross-correlated measurement and process noise; auto-correlated measurement noise; smoothing;

· Multiple Model adaptive Kalman filter; delayed-state filter; linearization

· Nonlinear filtering; the Extended Kalman Filter; simultaneous state and parameter estimation;

· Complementary filter: error model, total model;

· inertial navigation; position determination with GPS; the observables; receiver clock model;

· Kalman filter applications to the GPS; integer ambiguity resolution; tropospheric delay estimation; aided inertial navigation with conventional sensors and GPS

· Particle filters, terrain navigation.