EECE.6870 Applied Stochastic Estimation (Formerly 16.687)

Description

Review of random processes and key elements of probability theory. State space description of systems and random processes, relation to frequency domain techniques. Numerical methods of continuous and discrete time random system modeling. Optimal Kalman filtering for discrete and continuous random systems. Sensitivity analysis. Design considerations in the face of model uncertainty, numerical instabilities, bad data. Optimal smoothing. Nonlinear filtering. Parameter identification. Applications throughout.

Course prerequisites/corequisites are determined by the faculty and approved by the curriculum committees. Students are required to fulfill these requirements prior to enrollment. For courses offered through online or GPS delivery, students are responsible for confirming with the instructor or department that all enrollment requirements have been satisfied before registering.