Second-order EKF and Unscented Kalman Filter Fusion for Tracking Maneuvering Targets

When dealing with target tracking problem for maneuvering targets, it may be the case that a first order extended Kalman filter can not track the target and diverges due to neglecting the higher order terms of Taylor series. This paper studies two other filters which are more appropriate for maneuvering targets (with nonlinear state space equations). These two filters are entitled as second-order extended Kalman filter (SOEKF) and unscented Kalman filter (UKF). SOEKF uses Hessian matrix (second term of Taylor series) which may help solving the divergence problem. UKF is also useful as it works with the main nonlinear formula without the need to use any approximation. Both of the state space equations (process equation and measurement equation) is assumed to be nonlinear. In order to enhance the accuracy of tracking process sensor fusion approach is also applied for both of the filters. The number of sensors is assumed to be two. A comparison analysis is made between the two filters alone (without fusion approach) and also when sensor fusion is applied.