Kalman Filter References 
 

 
     Reading list 
 

1

Aström and P. Eykhoff, 1971; System identification, A survey, Automatica, V. 7, pp. 123-162

2

Bélanger, Pierre, 1974; Estimation of Noise Covariance Matrices for a Linear Time-Varying Stochastic Process, Automatica, V. 10, pp. 267 - 275

3

Berkhout, A.J. and P.R. Zaanen, 1976; A comparison between Wiener filtering, Kalman filtering, and deterministic least-squares estimation, Geophys. Prosp., V. 24, pp. 141-197

4

Bierman, G.L, 1974; Sequential square root filtering and smoothing of discrete linear systems, Automatica, V. 10, pp. 147-158

5

Bierman, G.L, 1977; Factorization methods for discrete sequential estimation, Mathematics in Science and Engineering, V. 128, Academic Press, New York

6

Brown, R.G. and Y.C. Hwang, 1992; Introduction to Random Signals and Applied Kalman Filtering, Second edition, John Wiley & Sons

7

Bryson, A.E., and Y-C Ho, 1975; Applied Optimal Control; optimization, estimation and control, Halsted Press (John Wiley & Sons), New York, 481 pages, ISBN 0-470-26774-7

8

Carlson, N.A, 1973; Faster triangular formulation of the square-root filter, AIAA J., V. 11, No. 9

9

Cohn, S., M. Ghil, and E. Isaacson, 1981; Optimal Interpolation and the Kalman Filter, Proceedings of the Fifth Conference on Numerical Weather Prediction, Monterey California, American Meterological Society, Boston Mass., pp. 36 - 42

10

Crump, N.D, 1974; A Kalman filter approach to the deconvolution of seismic signals, Geophysics, V. 39, pp. 1-13

11

Dean, G.C, 1986; An Introduction to Kalman filters Measurement and Control, V. 19, pp. 69 - 73

12

Dee, D.P., S.E. Cohn, A. Dalcher and M. Ghil, 1985; An Efficient Algorithm for Estimating Noise Covariances in Distributed Systems, IEEE Trans. Auto. Control, AC-30 No. 11, pp 1057 - 1065

13

du Plessis, R.M., 1967; Poor man's explanation of Kalman Filters or How I stopped worrying and learned to love matrix inversion, Rockwell International Technical Note, Anaheim, California, 24 pages

14

Dyer, P. and S. McReynolds, 1969; Extension of square-root filtering to include process noise, J. Optimization Theory and Applications, V. 3 No. 6, pp. 444-459

15

Gelb, A. (ed), 1974; Applied Optimal Estimation, MIT press, Cambridge Mass, 374 pages, ISBN 0 262 70008-5

16

Grewal, M.S. and A.P. Andrews, 1993; Kalman Filtering Theory and Practice, Prentice-Hall, Englewood Cliffs, New Jersey

17

Halpenny, J., 1984; A method of editing time series observations, Geophysics, V. 49. No. 5, pp. 521-524

18

Ho, Y.C, 1963; On the stochastic approximation method and optimal filtering theory, J. Math. Annal. Appl., V. 6, pp. 152 - 154

19

Special Issue on applications of Kalman filtering, IEEE Trans. Auto. Control, V. AC-28, No. 3,

20

Jacobs, O.L.R, 1993; Introduction to Control Theory, second edition, Oxford University Press

21

Jazwinski, A.H., 1970; Stochastic Processes and Filtering Theory, Academic Press, New York

22

Kailath, T., 1968; An innovations approach to least-squares estimation Part 1: Linear filtering in additive white noise, IEEE Trans. Auto. Control, AC-13, pp. 645-655

23

Kailath, T., 1974; A view of three decades of linear filtering theory IEEE Trans. Info. Theory, V. IT-20, pp. 146-181

24

Kalman, R.E., 1960; A new approach to linear filtering and prediction problems, Trans. ASME, Series D, J. Basic Eng., V. 82, March, pp. 35 - 45

25

Kalman, R.E., 1961; New Methods and Results in Linear Filtering and Prediction Theory, ASME Jour. Basic Engineering, Series D, V. 83

26

Kaminski P.F., A.E. Bryson, and S.F. Schmidt,1971; Discrete square root filtering: A survey of current techniques, IEEE Trans. Auto. Control, AC-16, pp. 727-736

27

Lewis, R., 1986; Optimal Estimation with an Introduction to Stochastic Control Theory, John Wiley & Sons

28

Lobdill, J., 1981; Kalman Mileage Predictor-Monitor, Byte, July, pp. 230 - 248

29

McGee, L.A. and S.F. Schmidt, 1985; Discovery of the Kalman Filter as a Practical Tool for Aerospace and Industry, NASA Technical Memorandum 86847, Moffett Field, California, 21 pages

30

Maybeck, P.S., 1979; Stochastic Models, Estimation, and Control, Volume 1, Academic Press.

31

Mehra, R.K., 1970; On the identification of variances and adaptive Kalman filtering IEEE Trans. Auto. Control, AC-15, pp. 175-184

32

Mehra, R.K., 1972; Approaches to adaptive filtering IEEE Trans. Auto. Control, AC-17, pp. 693-698

33

Mendel, J.M. and J. Kormylo, 1978; Single-Channel white-noise esimators for deconvolution, Geophysics, V. 43 No. 1, pp. 102-124

34

Ott, N. and H.G. Meder, 1972; The Kalman filter as a prediction error filter, Geophys. Prosp., V. 20, pp. 549-560

35

Sage, A.O. and G.W. Husa, 1969; Adaptive filtering with unknown prior statistics, JACC Boulder Colorado

36

Schmidt, S.F., 1981; The Kalman filter: Its recognition and development for aerospace applications, AIAA J. Guidance Control, V 4. No. 1, p. 4

37

Sorenson, H.W., 1970; Least-Squares estimation: from Gauss to Kalman IEEE Spectrum, V. 7, pp. 63-68

38

Wieslander, J. and B. Wittenmark, 1971; An approach to adaptive control using real-time identification, Automatica, V. 7, pp. 211-218

The note by du Plessis is a must have classic. You can now purchase copies of the du Plessis book from Taygeta Scientific!

The book edited by Gelb is the one book that anyone that does Kalman filtering should have on their shelf (where it is handy to get at).

 Everett (Skip) Carter            Phone: 831-641-0645 FAX:  831-641-0647
 Taygeta Scientific Inc.        INTERNET: skip@taygeta.com
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