EE 381K-6 Estimation Theory

Unscented Kalman filter:

S. J. Julier and J. K. Uhlmann, "A new extension of the Kalman filter to nonlinear systems."

E. A. Wan and R. Van Der Merwe, "The unscented Kalman filter."

Optimality of Kalman filter in linear dynamical systems with Gaussian inputs:

Richard J. Meinhold and Singpurwalla N. D., "Understanding the Kalman-Filter," The American Statistician, May 1983, 37, 2, 123-127.

M. Welling, "The Kalman Filter," class notes.

T. P. Minka, "From Hidden Markov Models to Linear Dynamical Systems," MIT Technical Report TR#531, 2000. (See Section 3).

Particle filtering tutorials:

P. Djuric et. al., "Particle Filtering",IEEE Signal Processing Magazine, vol. 20, no. 5, September 2003, pp: 19-38.

S. Arulampalam et. al., "A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking," IEEE Transactions on Signal Processing, vol. 50, no.2, February 2002, pp. 174-188.

On Cramer-Rao lower bound:

D. Johnson, "Cramer-Rao Bound," brief tutorial on cnx.org, 2003.

Markov Chain Monte Carlo (MCMC):

B. Walsh, "Markov Chain Monte Carlo and Gibbs Sampling,"Lecture notes, 2004.

P. Djuric et. al., "Perfect Sampling: A Review and Applications to Signal Processing,"IEEE Transactions on Signal Processing, vol. 50, no. 2, February 2002, pp. 345-356.

Further links:
Sequential Monte Carlo Methods (at Cambridge University).