@InProceedings{Supelec619,
author = {Matthieu Geist and Olivier Pietquin},
title = {Statistically Linearized Recursive Least Squares},
year = {2010},
booktitle = {Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)},
pages = {272 - 276},
month = {August-September},
address = {Kittilä (Finland)},
url = {http://www.metz.supelec.fr/metz/personnel/geist_mat/pdfs/Supelec619.pdf},
isbn = {978-1-4244-7875-0},
abstract = {This article proposes a new interpretation of the sigmapoint kalman filter (SPKF) for parameter estimation as being a statistically linearized recursive least-squares algorithm. This gives new insight on the SPKF for parameter estimation and particularly this provides an alternative proof for a result of Van der Merwe. On the other hand, it legitimates the use of statistical linearization and suggests many ways to use it for parameter estimation, not necessarily in a least-squares sens.}
}