Theory, analysis, and practical design and implementation of Kalman filters are covered, along with example applications to real-world problems. Topics include a review of random processes and linear system theory; Kalman filter derivations; divergence analysis; numerically robust forms; suboptimal filters and error budget analysis; prediction and smoothing; cascaded, decentralized, and federated filters; linearized, extended, second-order, and adaptive filters; and case studies in GPS, inertial navigation, and ballistic missile tracking.

Course prerequisite(s): 

525.614 Probability and Stochastic Processes for Engineers and 525.666 Linear System Theory or equivalents; knowledge of MATLAB (or equivalent software package).

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