Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

Improved Strong Tracking Cubature Kalman Filter for Target Tracking

Authors
Shuida Bao, An Zhang
Corresponding Author
Shuida Bao
Available Online June 2017.
DOI
https://doi.org/10.2991/caai-17.2017.35How to use a DOI?
Keywords
cubature Kalman filter; strong tracking filter; fault detection and isolation; numerical stability
Abstract
Cubature Kalman filter (CKF) is a very popular non-linear filter algorithm recently. CKF obtains better numerical stability and accuracy in high dimensional situation compared to UKF. However, in case of process model uncertainty, the performance of CKF will greatly degrade or even provoke divergence. An improved strong tracking CKF (ISTCKF) is proposed to keep the numerical stability and improve the robustness. First, the theoretical framework of strong tracking filter (STF) is combined with CKF. Then, an enhanced fault detection and isolation technique is established to overcome the drawback in STCKF. ISTCKF only performs correction phase when the process model uncertainty is detected and isolated. The ISTCKF is tested and validated via a target tracking model.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/caai-17.2017.35How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Shuida Bao
AU  - An Zhang
PY  - 2017/06
DA  - 2017/06
TI  - Improved Strong Tracking Cubature Kalman Filter for Target Tracking
BT  - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 164
EP  - 168
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.35
DO  - https://doi.org/10.2991/caai-17.2017.35
ID  - Bao2017/06
ER  -