Kalman Filtering Tutorial

Recommendations

Info

The covariance update Numerical Rounding Problems P( k + 1| k + 1) = P( k + 1| k ) − W( k + 1) S( k + 1) W( k + 1)' involves subtraction and can results in loss of symmetry and positive definiteness due to rounding errors. Joseph’s form covariance update avoids this at expense of computation burden: [ ] [ ] P( k + 1| k + 1) = I − W( k + 1) H( k + 1) P( k + 1| k) I − W( k + 1) H( k + 1) ' + W( k + 1) R( k + 1) W( k + 1)' Only subtraction is “squared” and preserves symmetry. 28
Extended <strong>Kalman</strong> Filter (EKF) Many practical systems have non-linear state update or measurement equations. The <strong>Kalman</strong> filter can be applied to a linearised version of these equations with loss of optimality: 29
Page 1 and 2: Understanding and Applying Kalman F
Page 3 and 4: What is a Kalman Filter and What Ca
Page 5 and 6: Word examples: • Determination of
Page 7 and 8: The covariance of a column vector x
Page 9 and 10: Error Ellipses corresponding to 50
Page 11 and 12: Formulating a Kalman Filter Problem
Page 13 and 14: FALLING BODY EXAMPLE Consider an ob
Page 15 and 16: ⎡1 1⎤ ⎡0.5⎤ x(k +1) = ⎢ x
Page 17 and 18: State Estimation 0. Known are x$( k
Page 19 and 20: Page 219 Bar-Shalom ANATOMY OF KALM
Page 21 and 22: {F, H} is completely observable if
Page 23 and 24: 101 99 97 95 93 91 89 87 True posit
Page 25 and 26: Kalman Filter Extensions • Valida
Page 27: Sequential Measurement Processing I
Page 31 and 32: Iterated Extended Kalman Filter (IE
Page 33 and 34: Kalman filtering classes, for defin
Page 35 and 36: Odometry Error Covariance Estimatio
Page 37: Scanned Monocular Sonar Sensing Sma

Kalman Filtering Tutorial

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?