Improved Strong Tracking Cubature Kalman Filter for Target Tracking
Shuida Bao, An Zhang
Available Online June 2017.
- https://doi.org/10.2991/caai-17.2017.35How to use a DOI?
- cubature Kalman filter; strong tracking filter; fault detection and isolation; numerical stability
- 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.
- 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 -