Kalman Filtering Tutorial

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Further Reading Bar-Shalom and Xiao-Rong Li, Estimation and Tracking: Principles, Techniques and Software, Artech House Boston, 1993. Jazwinski, A. H. . Stochastic Processes and Filtering Theory. New York, Academic Press, 1970. Bozic, S M, Digital and Kalman Filtering, Edward Arnold, London 1979. Maybeck, P. S. “The Kalman filter: An introduction to concepts.” Autonomous Robot Vehicles. I. J. Cox and G. T. Wilfong. New York, Springer-Verlag: 194- 204, 1990. 34
Odometry Error Covariance Estimation for Two Wheel Robot Vehicles (Technical Report MECSE-95-1, 1995) A closed form error covariance matrix is developed for (i) straight lines and (ii) constant curvature arcs (iii) turning about the centre of axle of the robot. Other paths can be composed of short segments of constant curvature arcs. Assumes wheel distance measurement errors are zero mean white noise. Previous work incrementally updates covariance matrix in small times steps. 35
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 and 28: Sequential Measurement Processing I
Page 29 and 30: Extended Kalman Filter (EKF) Many p
Page 31 and 32: Iterated Extended Kalman Filter (IE
Page 33: Kalman filtering classes, for defin
Page 37: Scanned Monocular Sonar Sensing Sma

Kalman Filtering Tutorial

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