PARTICLE FILTERING - Adaptive Signal Processing: Next Generation Solutions - page 330

42. S. Julier, J. Uhlmann, and H. F. Durante-White, A newmethod for nonlinear transformation
of means and covariances in filters and estimators, IEEE Trans. on Automatic Control , 45,
477 (2000).
43. R. E. Kalman, A new approach to linear filtering and prediction Pproblems, Trans. of the
ASME-Journal of Basic Engineering , 82, 35, (1960).
44. R. Karlsson and F. Gustafsson, Complexity analysis of the marginalized particle filter, IEEE
Trans. on Signal Processing , 53, 4408, (2005).
45. R. Karlsson and F. Gustafsson, Bayesian surface and underwater navigation, IEEE Trans.
on Signal Processing , 54, 4204, (2006).
46. G. Kitagawa, Non-Gaussian state-space modeling of nonstationary time series, Journal of
the American Statistical Association , 82, 1032, (1987).
47. G. Kitagawa, Monte Carlo filter and smoother for non-Gaussian nonlinear state space
models, Journal of Computational and Graphical Statistics , 1, 25, (1996).
48. T. Kloek and H. K. van Dijk, Bayesian estimates of system equation parameters: An appli-
cation of integration by Monte Carlo, Econometrica , 46, 1, (1978).
49. S. C. Kramer and H. W. Sorenson, Recursive Bayesian estimation using piece-wise constant
approximations, Automatica , 24, 789, (1988).
50. J. Kotecha and P. M. Djuri ´ , Gaussian particle filtering, IEEE Trans. on Signal Processing ,
51, 2592, (2003).
51. J. Kotecha and P. M. Djuri´, Gaussian sum particle filtering, IEEE Trans. on Signal
Processing , 51, 2602, (2003).
52. H. R. K¨nsch, “State space and hidden Markov models”, in O. E. Barndorff-Nielsen,
D. R. Cox, and C. Klueppelberg, Eds., In Complex Stochastic Systems , 109, Boca Raton:
CRC Publishers, (2001).
53. H. R. K¨nsch, Recursive Monte Carlo filters: Algorithms and theoretical analysis, The
Annals of Statistics , 33, 1983, (2005).
54. J. S. Liu and R. Chen, Sequential Monte Carlo methods for dynamical systems, Journal of
the American Statistical Association , 93, 1032, 1998.
55. J. S. Liu, Monte Carlo Strategies in Scientific Computing , Springer, New York, 2001.
56. J. Liu and M. West, “Combined parameter and state estimation in simulation-based filter-
ing,” in A. Doucet, N. de Freitas, and N. Gordon, Eds. Sequential Monte Carlo Methods
in Practice , 197, Springer, New York, (2001).
57. L. Ljung and T. S¨derestr¨m, Theory and Practice of Recursive Identification , Cambridge,
MA: The MIT Press, (1987).
58. A. Marshall, “The use of multi-stage sampling schemes in Monte Carlo computations”, in
M. Meyer, Ed., Symposium on Monte Carlo Methods , Wiley, pp. 123-140, (1956).
59. C. J. Masreliez, Approximate non-Gaussian filtering with linear state and observation
relations, IEEE Trans. on Automatic Control , 20, 107, (1975).
60. L. Mihaylova, D. Angelova, S. Honary, D. R. Bull, C.N. Canagarajah, and B. Ristic,
Mobility tracking in cellular networks using particle filtering, IEEE Trans. on Wireless
Communications , 6, 3589 (2007).
61. M. R. Morelande and S. Challa, Manoeuvering target tracking in clutter using particle fil-
ters, IEEE Trans. on Aerospace and Electronic Systems , 41, 252, (2005).