Transcript Lecture 23
ASEN 5070: Statistical Orbit Determination I Fall 2015 Professor Brandon A. Jones Lecture 23: Exam 1, Conceptual Filter Comparisons, and the EKF University of Colorado Boulder Homework 7 Due Friday Lecture Quiz ◦ Due by 5pm on Friday University of Colorado Boulder 2 Exam 1 Discussion University of Colorado Boulder 3 Comparison of Kalman and Batch University of Colorado Boulder 4 University of Colorado Boulder 5 University of Colorado Boulder 6 University of Colorado Boulder 7 University of Colorado Boulder 8 University of Colorado Boulder 9 University of Colorado Boulder 10 University of Colorado Boulder 11 What are the similarities between the batch and the sequential processor (as discussed up until now)? What are the differences between the batch and the sequential processor (as discussed up until now)? University of Colorado Boulder 12 Extended Kalman Filter (EKF) University of Colorado Boulder 13 No change to the reference trajectory with each observation! University of Colorado Boulder 14 One method of nonlinear filtering Update reference trajectory with each measurement update Reduces linearization errors University of Colorado Boulder 15 University of Colorado Boulder 16 The Batch and the CKF are mathematically equivalent Is the EKF equivalent to the Batch? Is it Bayes optimal? What happens if the updated reference trajectory is a poor approximation? Under what conditions could that happen? University of Colorado Boulder 17 Typically should not update reference trajectory immediately ◦ Initial estimate of state deviation may be unstable until the filter “converges” Must propagate the trajectory between each observation University of Colorado Boulder 18