Open Problems in Applications of the Kalman Filtering Algorithm

DOI

10.2991/mbdasm-19.2019.43How to use a DOI?

Keywords

Kalman filter; dynamic system; initial deviation; model disturbance; outliers

Abstract

In practical application, the Kalman filter (KF) still have technical problems which have not been solved in the LDS, such as the determination of filter initial values, the slight deviation of model coefficients, the outlier or systematic deviation of measurement data and the covariance estimation of model disturbance and measurement errors. Whether the above situations affect the KF estimation and its accuracy, it is a practical problem which is high-profile and unavoidable in the application of the KF. Therefore, in this paper, take a typical linear state-space model as object, Monte Carlo method is used to simulate and verify the above technical problems are not negligible under different bias conditions. The research results tell us that it is necessary to pay much attention to the influence of the initial deviation, the model coefficient deviation and outliers or systematic errors of measurement data on the KF in the LDS.

Copyright

© 2019, 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  - He Song
AU  - Shaolin Hu
PY  - 2019/10
DA  - 2019/10
TI  - Open Problems in Applications of the Kalman Filtering Algorithm
BT  - Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019)
PB  - Atlantis Press
SP  - 185
EP  - 190
SN  - 2352-538X
UR  - https://doi.org/10.2991/mbdasm-19.2019.43
DO  - 10.2991/mbdasm-19.2019.43
ID  - Song2019/10
ER  -