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SIAM Chapter Seminar

Sam Cohn
Carnegie Melllon University
Title: The Kalman Filter or: How We Learned to Stop Worrying and Land On the Moon

Abstract: The Stochastic Filtering problem is to find a "best prediction" of a signal or state of a dynamical system when all we can observe is a transformed version with random noise. The Kalman Filter is a solution for the case when future states of the signal depend linearly on the past ones, but has been shown to be practically effective for many nonlinear problems as well by means of linear approximation. If fact the first major application of the Kalman Filter was on board the Apollo 11 space shuttle for estimation of trajectories. In this talk, we will shortly discus the Stochastic Filtering problem in general and then derive the Kalman Filter which solves the linear case descibed above. (The talk will contain no rocket science other than scratchy diagrams.)

Date: Tuesday, April 28, 2015
Time: 5:30 pm
Location: Wean Hall 8220