As a follow-on course to “Kalman Filter Boot Camp”, this course derives the steps of the linear Kalman filter to give understanding regarding how to adjust the method to applications that violate the standard assumptions. Applies this understanding to enhancing the robustness of the filter and to extend to applications including prediction and smoothing.
Shows how to implement a target-tracking application in Octave code using an interacting multiple-model Kalman filter.
