Stochastic Estimation and Control
MIT OpenCourseWare's graduate course in estimation and control of dynamic systems, taught in the Department of Aeronautics and Astronautics. Topics move from reviews of probability and random variables into classical and state-space descriptions of random processes and how they propagate through linear systems, then into frequency domain design of filters and compensators. The core of the course is the Kalman filter, developed as a tool for estimating the states of dynamic systems, with later lectures covering stability conditions for the filter equations. Materials include lecture notes, assignments, and exams typical of MIT OCW offerings, free to access under a Creative Commons license with no certificate involved. The course suits students with a background in linear systems and probability who want a rigorous treatment of stochastic estimation theory.