Improved Strong Tracking Cubature Kalman Filter for Target Tracking

摘要

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.