TY - GEN
T1 - Increasing the convergence rate of theextended kalman filter
AU - Rhudy, Matthew
N1 - Publisher Copyright:
© 2015, American Institute of Aeronautics and Astronautics Inc. All rights received.
PY - 2015
Y1 - 2015
N2 - Efforts to improve the convergence of the Extended Kalman Filter (EKF) are presented. Three different scaling parameters are introduced which change the convergence properties of the estimation. Using an example nonlinear filtering problem, these scaling parameters are shown to increase the convergence rate of the EKF but at the cost of increased persistent estimation error. To remedy this, a time-varying scaling parameter is developed, which maintains the increased convergence rate of the filter without degrading the persistent estimation performance of the filter.
AB - Efforts to improve the convergence of the Extended Kalman Filter (EKF) are presented. Three different scaling parameters are introduced which change the convergence properties of the estimation. Using an example nonlinear filtering problem, these scaling parameters are shown to increase the convergence rate of the EKF but at the cost of increased persistent estimation error. To remedy this, a time-varying scaling parameter is developed, which maintains the increased convergence rate of the filter without degrading the persistent estimation performance of the filter.
UR - http://www.scopus.com/inward/record.url?scp=84937858955&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937858955&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84937858955
T3 - AIAA Infotech at Aerospace
BT - AIAA Infotech at Aerospace
PB - American Institute of Aeronautics and Astronautics Inc.
T2 - AIAA Infotech @ Aerospace 2015
Y2 - 5 January 2015 through 9 January 2015
ER -