TY - GEN
T1 - An adaptive split and merge unscented Gaussian sum filter for initial alignment of SINS
AU - Wang, Junhou
AU - Chen, Jiabin
PY - 2010
Y1 - 2010
N2 - In order to improve the performance of the unscented Kalman filter with uncertain or time-varying noise statistic, a novel adaptive split and merge unscented Gaussian sum filter is proposed for the initial alignment on the swaying base. The novel filter makes use of the output measurement information to online update the covariance of the process noise. A split technique is used to estimate the mean of the process noise. The updated mean and covariance are further feed back into the unscented Gaussian sum filter. The simulation results demonstrate that the novel filter is superior to the unscented Kalman filter.
AB - In order to improve the performance of the unscented Kalman filter with uncertain or time-varying noise statistic, a novel adaptive split and merge unscented Gaussian sum filter is proposed for the initial alignment on the swaying base. The novel filter makes use of the output measurement information to online update the covariance of the process noise. A split technique is used to estimate the mean of the process noise. The updated mean and covariance are further feed back into the unscented Gaussian sum filter. The simulation results demonstrate that the novel filter is superior to the unscented Kalman filter.
UR - http://www.scopus.com/inward/record.url?scp=78649306403&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2010.5588977
DO - 10.1109/ICMA.2010.5588977
M3 - Conference contribution
AN - SCOPUS:78649306403
SN - 9781424451418
T3 - 2010 IEEE International Conference on Mechatronics and Automation, ICMA 2010
SP - 1892
EP - 1897
BT - 2010 IEEE International Conference on Mechatronics and Automation, ICMA 2010
T2 - 2010 IEEE International Conference on Mechatronics and Automation, ICMA 2010
Y2 - 4 August 2010 through 7 August 2010
ER -