Page 870 - The Mechatronics Handbook

0066_Frame_C28 Page 10 Wednesday, January 9, 2002 7:19 PM

of a state estimate in the presence of accurate measurement information. However, batch estimation
techniques such as least-squares estimation may be more appropriate in applications where the dynamic
process is modeled to a high degree of fidelity, measurements are not uniformly accurate, and real-time
operation is not an issue. A number of quality texts [10–12] have been written on the subject of stochastic
estimation in general and specifically Kalman filtering that the the reader is encouraged to pursue for
more detailed information.

References
1. Kalman, R. E., “A new approach to linear filtering and prediction problems,” Transactions of the
ASME, Ser. D, Journal of Basic Equations, March 1960, pp. 35–45.
2. Burkhart, P. and Bishop, R., “Adaptive orbit determination for interplanetary spacecraft,” Journal of
Guidance, Control, and Dynamics, Vol. 19, No. 3, 1997, pp. 693–701.
3. Chaer, W., Bishop, R., and Ghosh, J., “Hierarchical adaptive Kalman filtering for interplanetary
orbit determination,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 34, No. 3, 1998,
pp. 1–14.
4. Crain, T. and Bishop, R., “The mixture-of-experts gating network: integration into the ARTSN
extended Kalman filter,” Technical Memorandum CSR-TM-99-01, Center for Space Research, March
1999.
5. Ely, T., Bishop, R., and Crain, T., “Adaptive interplanetary navigation using genetic algorithms,” The
Journal of Astronautical Sciences, 2000, Accepted for Publication.
6. Crain, T. and Bishop, R., “Unmodeled impulse detection and identification during Mars pathfinder
cruise,” Technical Memorandum CSR-TM-00-01, Center for Space Research, March 2000.
7. Chaer, W. and Bishop, R., “Adaptive Kalman filtering with genetic algorithms,” Advances in the
Astronautical Sciences, edited by R. Proulx, J. Liu, P. Siedelmann, and S. Alfano, Vol. 89, Univelt, San
Diego, CA, 1995, pp. 141–156, Pt. 1.
8. Gholson, N. and Moose, R., “Maneuvering target tracking using adaptive state estimation,” IEEE
Transactions on Aerospace and Electronic Systems, Vol. 13, No. 3, May 1997, pp. 310–317.
9. Bierman, G., Factorization Methods for Discrete Sequential Estimation, Academic Press, 1977.
10. Brown, R. G. and Huang, P. Y. C., Introduction to Random Signals and Applied Kalman Filtering, John
Wiley and Sons, 1992.
11. Lewis, F., Applied Optimal Control and Estimation, Prentice-Hall, Englewood Clifis, NJ, 1992.
12. Gelb, A., Applied Optimal Estimation, The M.I.T. Press, Cambridge, MA, 1974.

©2002 CRC Press LLC