@inproceedings{afe7d51bbaca40d6835cd1c98cebf5d1,
title = "Iterative state estimation",
abstract = "Iterative solvers allow for a trade-off between speed and accuracy. We propose an iterative method for the estimation of the internal states of a given discrete-time linear state-space model from a series of noisy measurements. In particular we identify the MAP estimate of those states as being the solution of a sparse system of linear equations and derive an iterative solver based on the conjugate gradient method. We derive convergence results to quantify the trade-off between speed and accuracy and finally apply the method to channel estimation where it is shown to outperform Kalman smoothing complexity-wise.",
keywords = "conjugate gradient method, Kalman smoothing, state estimation, state space systems",
author = "Riedl, \{Thomas J.\} and Singer, \{Andrew C.\}",
year = "2010",
doi = "10.1109/ACSSC.2010.5757881",
language = "English",
isbn = "9781424497218",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
pages = "1956--1958",
booktitle = "Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010",
note = "44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 ; Conference date: 07-11-2010 Through 10-11-2010",
}