Development and numerical investigation of new non-linear Kalman filter variants

IET Control Theory and Applications

Volumn 5, Issue 10, 2011, Pages 1155-1166

  Charalampidis, A C ^a   Papavassilopoulos, G P ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION ERRORS; MODE-BASED; NON-LINEAR; NUMERICAL INVESTIGATIONS; PARTICLE FILTER; RECURSIVE STATE ESTIMATION; SAMPLING IMPORTANCE RESAMPLING; STATE ESTIMATORS; UNSCENTED KALMAN FILTER;

ESTIMATION; LINEAR SYSTEMS; NONLINEAR FILTERING; STATE ESTIMATION;

KALMAN FILTERS;

EID: 79960695949     PISSN: 17518644     EISSN: 17518652     Source Type: Journal    
DOI: 10.1049/iet-cta.2010.0553     Document Type: Article

References (20)

1. Kalman, R.E.: ' A new approach to linear filtering and prediction problem ', ASME J. Basic Eng., 1960, 82, (1), p. 35-45
2. 'Application of statistical filter theory to the optimal estimation of position and velocity on board a circumlunar vehicle ', Technical Report TR R-135, NASA, 1962
3. Julier, S., and Uhlmann, J.K.: 'Unscented filtering and nonlinear estimation ', Proc. IEEE, 2004, 92, (3), p. 401-422 10.1109/JPROC.2003.823141 0018-9219 (Pubitemid 40890750)
4. Sanjeev Arulampalam, M., Maskel, S., Gordon, N., and Clapp, T.: 'A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking ', IEEE Trans. Signal Process., 2002, 50, (2), p. 174-188 10.1109/78.978374 1053-587X (Pubitemid 34291500)
5. Cappé, O., Godsill, S.J., and Moulines, E.: 'An overview of existing methods and recent advances in sequential Monte Carlo ', Proc. IEEE, 2007, 95, (5), p. 899-924 10.1109/JPROC.2007.893250 0018-9219
6. Lefebvre, T., Bruyninckx, H., and Schutter, J.d.: 'Kalman filters for non-linear systems: a comparison of performance ', Int. J. Control, 2004, 77, (7), p. 639-653 10.1080/00207170410001704998 0020-7179
7. Julier, S., Uhlmann, J.K., and Durrant-Whyte, H.F.: 'A new method for the nonlinear transformation of means and covariances in filters and estimators ', IEEE Trans. Autom. Control, 2000, 45, (3), p. 477-482 10.1109/9.847726 0018-9286
8. Bayes, T.: 'An essay towards solving a problem in the doctrine of chances ', Phil. Trans. R. Soc. London, 1763, 53, p. 370-418 10.1098/rstl.1763.0053
9. Billingsley, P.: 'Probability and measure ', 3rd (Wiley-Interscience, Hoboken, NJ, 1995)
10. Crisan, D., and Doucet, A.: 'A survey of convergence results on particle filtering methods for practitioners ', IEEE Trans. Signal Process., 2002, 50, (3), p. 736-746 10.1109/78.984773 1053-587X (Pubitemid 34295113)
11. Hu, X.-L., Schon, T.B., and Ljung, L.: 'A basic convergence result for particle filtering ', IEEE Trans. Signal Process., 2008, 56, (4), p. 1337-1348 10.1109/TSP.2007.911295 1053-587X (Pubitemid 351493831)
12. 'The unscented particle filter ', INFENF/TR 380, August, 2000
13. Gordon, N.J., Salmond, D.J., and Smith, A.F.M.: 'Novel approach to nonlinear/non-Gaussian state estimation ', IEE Proc.-F, 1993, 140, (2), p. 107-113 10.1049/ip-f-2.1993.0015
14. Kitagawa, G.: 'Monte Carlo filter and smoother for non-Gaussian nonlinear state space modules ', J. Comput. Graph. Stat., 1996, 5, (1), p. 1-25 10.2307/1390750
15. Cramer, H.: 'Mathematical methods of statistics ', (Princeton University Press, Princeton, NJ, 1946)
16. Rao, C.: 'Information and the accuracy attainable in the estimation of statistical parameters ', Bull. Calcutta Math. Soc., 1945, 37, p. 81-89 0008-0659
17. van Trees, H.L.: 'Detection, estimation and modulation theory ', (Wiley, New York, 1968)
18. Tichavsky, P., Muravchik, C.H., and Nehorai, A.: 'Posterior Cramer-Rao bounds for discrete-time nonlinear filtering ', IEEE Trans. Signal Process., 1998, 46, (5), p. 1386-1396 10.1109/78.668800 1053-587X (Pubitemid 128743876)
19. Coleman, T.F., and Li, Y.: 'An interior, trust region approach for nonlinear minimization subject to bounds ', SIAM J. Optim., 1996, 6, p. 418-445 10.1137/0806023 1052-6234
20. Coleman, T.F., and Li, Y.: 'On the convergence of reflective newton methods for large-scale nonlinear minimization subject to bounds ', Math. Program., 1994, 67, (2), p. 189-224 10.1007/BF01582221 0025-5610