Levenberg-Marquardt Method Based Iterative Square Root Cubature Kalman Filter and its Applications to Maneuvering Re-entry Target Tracking

DOI

10.2991/icsnce-18.2018.1How to use a DOI?

Keywords

Nonlinear Filtering; Square Root Cubature Kalman Filter; Levenberg-Marquardt Method; Maneuvering Re-entry Targets Tracking

Abstract

Levenberg-Marquardt (abbr.L-M) method based iterative square root cubature Kalman filter (abbr. ISRCKFLM) inherits the numerical stability of square root Cubature Kalman filter and effectively suppresses the influence of the larger initial estimation error and the nonlinearity of the measurement equation on the state estimation in the nonlinear state estimation due to obtaining the optimal state and variance estimates using the latest measurement through L-M method. We apply the ISRCKFLM algorithm to the state estimation of maneuvering re-entry target tracking, the simulation results demonstrate that the ISRCKFLM algorithm has better accuracy of state estimation, comparable to Unscented Kalman filter and square root Cubature Kalman filter, according to estimation error analysis of the position, velocity, drag coefficient, turn coefficient and climbing force coefficient, and has fast convergence rate.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Mu Jing
AU  - Wang Changyuan
PY  - 2018/04
DA  - 2018/04
TI  - Levenberg-Marquardt Method Based Iterative Square Root Cubature Kalman Filter and its Applications to Maneuvering Re-entry Target Tracking
BT  - Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018)
PB  - Atlantis Press
SP  - 1
EP  - 4
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsnce-18.2018.1
DO  - 10.2991/icsnce-18.2018.1
ID  - Jing2018/04
ER  -