Recursive update filtering for nonlinear estimation

IEEE Transactions on Automatic Control

Volumn 57, Issue 6, 2012, Pages 1481-1490

  Zanetti, Renato ^a  

^a CHARLES STARK DRAPER LABORATORY   (United States)

Author keywords

Estimation; filtering; Kalman filtering; nonlinear estimation; recursive Kalman filter

Indexed keywords

COMPUTATIONAL COSTS; KALMAN-FILTERING; LINEAR ESTIMATORS; NON-LINEAR ESTIMATION; NONLINEAR ESTIMATOR; NONLINEAR FILTER; REAL-TIME APPLICATION; REAL-TIME NAVIGATION; RECURSIONS; RECURSIVE KALMAN; RECURSIVE UPDATE; TUNING PARAMETER; UNSCENTED KALMAN FILTER;

ALGORITHMS; BEARINGS (MACHINE PARTS); ESTIMATION; FILTRATION; KALMAN FILTERS;

NONLINEAR ANALYSIS;

EID: 84861754178     PISSN: 00189286     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAC.2011.2178334     Document Type: Article

References (16)

1. R. E. Kalman, "A new approach to linear filtering and prediction problems," J. Basic Eng., ser. D, vol. 82, pp. 35-45, Mar. 1960.
2. R. E. Kalman and R. S. Bucy, "New results in linear filtering and prediction," J. Basic Eng., ser. D, vol. 83, pp. 95-108, Mar. 1961.
3. B. D. Tapley, B. E. Schutz, and G. H. Born, Statistical Orbit Determination. Waltham, MA: Elsevier Academic Press, 2004.
4. H. W. Sorenson and D. L. Alspach, "Recursive bayesian estimation using gaussian sums," Automatica, vol. 7, no. 4, pp. 465-479, Jul. 1971.
5. A. Gelb, Ed., Applied Optimal Estimation. Cambridge, MA: MIT press, 1996, pp. 182-189, and pp. 190-191.
6. A. H. Jazwinski, Stochastic Processes and Filtering Theory, ser. Mathematics in Sciences and Engineering. New York: Academic, 1970, vol. 64, pp. 344-346, 10003.
7. D. Alspach and H. Sorenson, "Nonlinear Bayesian estimation using Gaussian sum approximations," IEEE Trans. Autom. Control, vol. 17, no. 4, pp. 439-448, Aug. 1972.
8. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," Radar and Signal Process., IEE Proc. F, vol. 140, no. 2, pp. 107-113, Apr. 1993.
9. P. S. Mayback, Stochastic Models, Estimation, and Control Volume 2, ser. Mathematics in Science and Engineering. New York: Academic, 1982.
10. S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, "A new method for the nonlinear transformation of means and covariances in filters and estimators," IEEE Trans. Autom. Control, vol. 45, no. 3, pp. 477-482, Mar. 2000.
11. R. Zanetti, K. J. DeMars, and R. H. Bishop, "Underweighting nonlinear measurements," J. Guidance, Control, Dyn., vol. 33, no. 5, pp. 1670-1675, Sep.-Oct. 2010.
12. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 1st ed. New York: McGraw-Hill, 1965, sec. 7-4 and 8-2.
13. R. G. Brown and P. Y.Hwang, Introduction to Random Signals and Applied Kalman Filtering, 3rd ed. NewYork:Wiley, 1997, pp. 348-350.
14. P. J. Huxel and R. H. Bishop, "Fusing inertial and relative measurements for inertial navigation in the presence of large state error covariances," in Proc. 16th AAS/AIAA Spaceflight Mech. Meeting, Jan. 2006.
15. B. M. Bell and F. W. Cathey, "The iterated Kalman filter update as a Gauss-Newton method," IEEE Trans. Autom. Control, vol. 38, no. 2, pp. 294-297, Feb. 1993.
16. J. E. Dennis and R. B. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, ser. Computational Mathematics. Englewood Cliffs, NJ: Prentice-Hall, 1983.