@inproceedings{b6480e6afd954a09ba2e7bbe36eb0a55,
title = "Adaptive unscented Kalman filter for initial alignment of strapdown inertial navigation systems",
abstract = "In order to improve the performance of the unscented Kalman filter with uncertain or time-varying noise statistic, a novel adaptive unscented Kalman filter with noise statistic estimator is utilized to initial alignment on the swaying base. This noise statistic estimator makes use of the output measurement information to online update the mean and the covariance of the noise. The updated mean and covariance are further feed back into the normal unscented Kalman filter. The simulation results demonstrate that the adaptive unscented Kalman filter is superior to the unscented Kalman filter.",
keywords = "Adaptive unscented Kalman filter, Initial alignment, Strap down inertial navigation system",
author = "Wang, \{Jun Hou\} and Chen, \{Jia Bin\}",
year = "2010",
doi = "10.1109/ICMLC.2010.5580847",
language = "English",
isbn = "9781424465262",
series = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",
publisher = "IEEE Computer Society",
pages = "1384--1389",
booktitle = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",
address = "United States",
}