@inbook{33e6d539de294ec7a992840d4941b496,
title = "The Kalman Filter",
abstract = "The Kalman filter and its variants are some of the most popular tools in statistical signal processing and estimation theory. In this chapter, we introduce the Kalman filter, providing a succinct, yet rigorous derivation thereof, which is based on the orthogonality principle. We also introduce several important variants of the Kalman filter, namely various Kalman smoothers, a Kalman predictor, a nonlinear extension (the extended Kalman filter), and adaptation to cases of temporally correlated measurement noise. The application of the Kalman filter to two important speech processing problems, namely, speech enhancement and speakerlocalization speaker localization is demonstrated.",
keywords = "Extend Kalman Filter, Kalman Filter, Mean Square Error, Speech Signal, Unscented Kalman Filter",
author = "Sharon Gannot and Arie Yeredor",
note = "Publisher Copyright: {\textcopyright} 2008, Springer-Verlag Berlin Heidelberg.",
year = "2008",
doi = "10.1007/978-3-540-49127-9_8",
language = "אנגלית",
series = "Springer Handbooks",
publisher = "Springer",
pages = "135--160",
booktitle = "Springer Handbooks",
}