Bibliography

Abramowitz, M. and Stegun, I. A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover, New York, ninth Dover printing, tenth GPO edition.

Arasaratnam, I. (2009). Cubature Kalman Filtering: Theory & Applications. PhD thesis, ECE Department, McMaster University.

Arasaratnam, I. and Haykin, S. (2009). Cubature Kalman filters. IEEE Transactions on Automatic Control, 54(6):1254–1269.

Bar-Shalom, Y., Li, X.-R., and Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation. Wiley Interscience.

Cools, R. (1997). Constructing cubature formulae: The science behind the art.

Acta Numerica, 6:1–54.

Cox, H. (1964). On the estimation of state variables and parameters for noisy dynamic systems. IEEE Transactions on Automatic Control, 9(1):5–12.

Fraser, D. C. and Potter, J. E. (1969). The optimum linear smoother as a combination of two optimum linear filters. IEEE Transactions on Automatic Control, 14(4):387–390.

Gelb, A., editor (1974). Applied Optimal Estimation. The MIT Press.

Gordon, N. J., Salmond, D. J., and Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEEE Proceedings on Radar and Signal Processing, volume 140, pages 107–113.

Grewal, M. S. and Andrews, A. P. (2001). Kalman Filtering, Theory and Practice Using MATLAB. Wiley Interscience.

Helmick, R., Blair, W., and Hoffman, S. (1995). Fixed-interval smoothing for Markovian switching systems. IEEE Transactions on Information Theory, 41(6):1845–1855.

Ito, K. and Xiong, K. (2000). Gaussian filters for nonlinear filtering problems.

IEEE Transactions on Automatic Control, 45(5):910–927.

127

BIBLIOGRAPHY

Jazwinski, A. H. (1966). Filtering for nonlinear dynamical systems. IEEE Transactions on Automatic Control, 11(4):765–766.

Julier, S. J. and Uhlmann, J. K. (2004a). Corrections to unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(12):1958–1958.

Julier, S. J. and Uhlmann, J. K. (2004b). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3):401–422.

Julier, S. J., Uhlmann, J. K., and Durrant-Whyte, H. F. (1995). A new approach for filtering nonlinear systems. In Proceedings of the 1995 American Control, Conference, Seattle, Washington, pages 1628–1632.

Kalman, R. E. (1960). A new approach to linear filtering and prediction problems.

Transactions of the ASME, Journal of Basic Engineering, 82:35–45.

Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5:1–25.

Kotecha, J. and Djuric, P. (2003). Gaussian particle filtering. IEEE Transactions on Signal Processing, 51(10):2592–2601.

Maybeck, P. (1982). Stochastic Models, Estimation and Control, Volume 2. Academic Press.

Rauch, H. E., Tung, F., and Striebel, C. T. (1965). Maximum likelihood estimates of linear dynamic systems. AIAA Journal, 3(8):1445–1450.

Sage, A. P. and Melsa, J. L. (1971). Estimation Theory with Applications to Communications and Control. McGraw-Hill Book Company.

Särkkä, S. (2006). Recursive Bayesian inference on stochastic differential equations. Doctoral dissertion, Helsinki University of Technology.

Särkkä, S. (2007). On unscented Kalman filtering for state estimation of continuous-time nonlinear systems. IEEE Transactions on Automatic Control, 52(9):1631–1641.

Särkkä, S. (2008). Unscented rauch-tung-striebel smoother. IEEE Transactions on Automatic Control, 53(3):845–849.

Särkkä, S. and Hartikainen, J. (2010). On gaussian optimal smoothing of non-linear state space models. IEEE Transactions on Automatic Control, 55(8):1938–1941.

Särkkä, S., Vehtari, A., and Lampinen, J. (2007). Rao-Blackwellized particle filter for multiple target tracking. Information Fusion, 8(1):2–15.

Solin, A. (2010). Cubature Integration Methods in Non-Linear Kalman Filtering and Smoothing. Bachelor’s thesis, Faculty of Information and Natural Sciences, Aalto University, Finland.

128

BIBLIOGRAPHY

Vanhatalo, J. and Vehtari, A. (2006). MCMC methods for MLP-networks and Gaussian process and stuff — a documentation for Matlab toolbox MCMCstuff. Toolbox can be found at http://www.lce.hut.fi/research/mm/mcmcstuff/.

Wan, E. A. and van der Merwe, R. (2001). The unscented Kalman filter. In Haykin, S., editor, Kalman Filtering and Neural Networks, chapter 7. Wiley.

Wu, Y., Hu, D., Wu, M., and Hu, X. (2005). Unscented Kalman filtering for additive noise case: Augmented versus nonaugmented. IEEE Signal Processing Letters, 12(5):357–360.

Wu, Y., Hu, D., Wu, M., and Hu, X. (2006). A numerical-integration perspective on Gaussian filters. IEEE Transactions on Signal Processing, 54(8):2910–2921.

129