6 Fundamentals of Filtering
221
20. A. Doucet, N. De Freitas, N. Gordon, Sequential Monte Carlo Methods in Practice, 1st edn.
(Springer, New York, 2001). https://doi.org/10.1007/978-1-4757-3437-9
21. A. Gelb, Applied Optimal Estimation (MIT Press, Cambridge, 1974)
22. W. Gilks, C. Berzuini, Following a moving target—Monte Carlo inference for dynamic
Bayesian models. J. Royal Stat. Soc. 63, 127–146 (2002). https://doi.org/10.1111/1467-9868.
00280
23. W. Gilks, S. Richardson, D. Spiegelhalter, Markov Chain Monte Carlo in Practice (Chapman
and Hall/CRC, Boca Raton, 1996)
24. N.J. Gordon, D.J. Salmond, A.F.M. Smith, Novel approach to nonlinear/non-Gaussian
Bayesian state estimation. IEE Proc. F (Radar and Signal Processing) 140(2), 107–113 (1993).
https://doi.org/10.1049/ip-f-2.1993.0015
25. M.S. Grewal, A.P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 4th edn.
(Wiley-IEEE Press, Hoboken, 2014)
26. A.H. Haug, Bayesian Estimation and Tracking: A Practical Guide (Wiley, Hoboken, 2012)
27. G. Hendeby, F. Gustafsson, On nonlinear transformations of stochastic variables and its
application to nonlinear filtering, in 2008 IEEE International Conference on Acoustics, Speech
and Signal Processing (2008). https://doi.org/10.1109/ICASSP.2008.4518435
28. K. Ito, K. Xiong, Gaussian filters for nonlinear filtering problems. IEEE Trans. Autom. Control.
45(5), 910–927 (2000)
29. A.H. Jazwinski, Stochastic Processes and Filtering Theory, ed. by R. Bellman. Mathematics
in Science and Engineering, vol. 64, 1st edn. (Academic, New York, 1970)
30. S. Julier, The spherical simplex unscented transformation, in Proceedings of the American
Control Conference, vol. 3 (2003). https://doi.org/10.1109/ACC.2003.1243439
31. S. Julier, J.K. Uhlmann, A general method for approximating nonlinear transformations of
probability distributions. Technical Report Robotics Research Group, University of Oxford
(1996)
32. S. Julier, J.K. Uhlmann, A consistent, debiased method for converting between polar and
Cartesian coordinate systems, in The Proceedings of AeroSense: The 11th International
Symposium on Aerospace/Defense Sensing, Simulation and Controls (1997)
33. S. Julier, J.K. Uhlmann, Reduced sigma point filters for the propagation of means and
covariances through nonlinear transformations, in Proceedings of the American Control
Conference, vol. 2 (2002). https://doi.org/10.1109/ACC.2002.1023128
34. S. Julier, J.K. Uhlmann, Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–
422 (2004). https://doi.org/10.1109/JPROC.2003.823141
35. T. Kailath, A.H. Sayed, B. Hassibi, Linear Estimation, 1st edn. (Prentice Hall, Upper Saddle
River, 2000)
36. R.E. Kalman, A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1),
35–45 (1960). https://doi.org/10.1115/1.3662552
37. D. Knill, W. Richards, Perception as Bayesian Inference, 1st edn. (Cambridge University Press,
Cambridge, 1996). https://doi.org/10.1017/CBO9780511984037
38. J.S. Liu, Monte Carlo Strategies in Scientific Computing, 1st edn. (Springer, New York, 2004)
39. J.S. Liu, R. Chen, Blind deconvolution via sequential imputations. J. Amer. Stat. Assoc.
90(430), 567–576 (1995)
40. M. Majji, J.L. Junkins, J.D. Turner, A perturbation method for estimation of dynamic systems.
Nonlinear Dyn. 60(3), 303–325 (2010). https://doi.org/10.1007/s11071-009-9597-6
41. A.M. Mathai, S.B. Provost, Quadratic Forms in Random Variables (CRC Press, Boca Raton,
1992)
42. P. Maybeck, Stochastic Models, Estimation and Control, vol. 2 (Academic, Cambridge, 1982)
43. B.A. McElhoe, An assessment of the navigation and course corrections for a manned flyby of
mars or venus. IEEE Trans. Aerosp. Electron. Syst. AES-2, 613–623 (1966)
44. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equation of state
calculations by fast computing machines. J. Chem. Phys. 21, 1087 (1953). https://doi.org/10.
1063/1.1699114
221
20. A. Doucet, N. De Freitas, N. Gordon, Sequential Monte Carlo Methods in Practice, 1st edn.
(Springer, New York, 2001). https://doi.org/10.1007/978-1-4757-3437-9
21. A. Gelb, Applied Optimal Estimation (MIT Press, Cambridge, 1974)
22. W. Gilks, C. Berzuini, Following a moving target—Monte Carlo inference for dynamic
Bayesian models. J. Royal Stat. Soc. 63, 127–146 (2002). https://doi.org/10.1111/1467-9868.
00280
23. W. Gilks, S. Richardson, D. Spiegelhalter, Markov Chain Monte Carlo in Practice (Chapman
and Hall/CRC, Boca Raton, 1996)
24. N.J. Gordon, D.J. Salmond, A.F.M. Smith, Novel approach to nonlinear/non-Gaussian
Bayesian state estimation. IEE Proc. F (Radar and Signal Processing) 140(2), 107–113 (1993).
https://doi.org/10.1049/ip-f-2.1993.0015
25. M.S. Grewal, A.P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, 4th edn.
(Wiley-IEEE Press, Hoboken, 2014)
26. A.H. Haug, Bayesian Estimation and Tracking: A Practical Guide (Wiley, Hoboken, 2012)
27. G. Hendeby, F. Gustafsson, On nonlinear transformations of stochastic variables and its
application to nonlinear filtering, in 2008 IEEE International Conference on Acoustics, Speech
and Signal Processing (2008). https://doi.org/10.1109/ICASSP.2008.4518435
28. K. Ito, K. Xiong, Gaussian filters for nonlinear filtering problems. IEEE Trans. Autom. Control.
45(5), 910–927 (2000)
29. A.H. Jazwinski, Stochastic Processes and Filtering Theory, ed. by R. Bellman. Mathematics
in Science and Engineering, vol. 64, 1st edn. (Academic, New York, 1970)
30. S. Julier, The spherical simplex unscented transformation, in Proceedings of the American
Control Conference, vol. 3 (2003). https://doi.org/10.1109/ACC.2003.1243439
31. S. Julier, J.K. Uhlmann, A general method for approximating nonlinear transformations of
probability distributions. Technical Report Robotics Research Group, University of Oxford
(1996)
32. S. Julier, J.K. Uhlmann, A consistent, debiased method for converting between polar and
Cartesian coordinate systems, in The Proceedings of AeroSense: The 11th International
Symposium on Aerospace/Defense Sensing, Simulation and Controls (1997)
33. S. Julier, J.K. Uhlmann, Reduced sigma point filters for the propagation of means and
covariances through nonlinear transformations, in Proceedings of the American Control
Conference, vol. 2 (2002). https://doi.org/10.1109/ACC.2002.1023128
34. S. Julier, J.K. Uhlmann, Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–
422 (2004). https://doi.org/10.1109/JPROC.2003.823141
35. T. Kailath, A.H. Sayed, B. Hassibi, Linear Estimation, 1st edn. (Prentice Hall, Upper Saddle
River, 2000)
36. R.E. Kalman, A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1),
35–45 (1960). https://doi.org/10.1115/1.3662552
37. D. Knill, W. Richards, Perception as Bayesian Inference, 1st edn. (Cambridge University Press,
Cambridge, 1996). https://doi.org/10.1017/CBO9780511984037
38. J.S. Liu, Monte Carlo Strategies in Scientific Computing, 1st edn. (Springer, New York, 2004)
39. J.S. Liu, R. Chen, Blind deconvolution via sequential imputations. J. Amer. Stat. Assoc.
90(430), 567–576 (1995)
40. M. Majji, J.L. Junkins, J.D. Turner, A perturbation method for estimation of dynamic systems.
Nonlinear Dyn. 60(3), 303–325 (2010). https://doi.org/10.1007/s11071-009-9597-6
41. A.M. Mathai, S.B. Provost, Quadratic Forms in Random Variables (CRC Press, Boca Raton,
1992)
42. P. Maybeck, Stochastic Models, Estimation and Control, vol. 2 (Academic, Cambridge, 1982)
43. B.A. McElhoe, An assessment of the navigation and course corrections for a manned flyby of
mars or venus. IEEE Trans. Aerosp. Electron. Syst. AES-2, 613–623 (1966)
44. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equation of state
calculations by fast computing machines. J. Chem. Phys. 21, 1087 (1953). https://doi.org/10.
1063/1.1699114