TY - GEN
T1 - Nonlinear fisher particle output feedback control and its application to terrain aided navigation
AU - Flayac, Emilien
AU - Dahia, Karim
AU - Herisse, Bruno
AU - Jean, Frederic
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/28
Y1 - 2017/6/28
N2 - This paper presents state estimation and stochastic optimal control gathered in one global optimization problem generating dual effect i.e. the control can improve the future estimation. As the optimal policy is impossible to compute, a sub-optimal policy that preserves this coupling is constructed thanks to the Fisher Information Matrix (FIM) and a Particle Filter. This method has been applied to the localization and guidance of a drone over a known terrain with height measurements only. The results show that the new method improves the estimation accuracy compared to nominal trajectories.
AB - This paper presents state estimation and stochastic optimal control gathered in one global optimization problem generating dual effect i.e. the control can improve the future estimation. As the optimal policy is impossible to compute, a sub-optimal policy that preserves this coupling is constructed thanks to the Fisher Information Matrix (FIM) and a Particle Filter. This method has been applied to the localization and guidance of a drone over a known terrain with height measurements only. The results show that the new method improves the estimation accuracy compared to nominal trajectories.
UR - https://www.scopus.com/pages/publications/85046292975
U2 - 10.1109/CDC.2017.8263874
DO - 10.1109/CDC.2017.8263874
M3 - Conference contribution
AN - SCOPUS:85046292975
T3 - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
SP - 1566
EP - 1571
BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 56th IEEE Annual Conference on Decision and Control, CDC 2017
Y2 - 12 December 2017 through 15 December 2017
ER -