@inproceedings{25c51da474e040c2b3780ba38bb2e403,
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 = "Kalman smoothing, conjugate gradient method, state estimation, state space systems",
author = "Riedl, {Thomas J.} and Singer, {Andrew C.}",
year = "2010",
doi = "10.1109/ACSSC.2010.5757881",
language = "English (US)",
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",
}