HIGH-PERFORMANCE TARGET TRACKING USING TRACKER FUSION IN A TRACK-WHILE-SCAN RADAR

The main problem that appears with tracking high-performance targets is the severe random change in the target motion in the model used by a uni-modal filter; e.g., Kalman filter. The interacting multiple model (IMM) can be considered as a multi-modal approach that solves Kalman filter problems with nonlinear target models. Though, it still requires prior knowledge about the target model as well as limited to white Gaussian noise. An adaptive filter that is able to deal with nonlinear models as well as colored noise is the particle filter. The efficiency and accuracy of the particle filter and its extensions depend mainly on two key factors: the propagation function used to re-allocate the particles and the number of particles used to estimate the posterior distribution. High performance targets with maneuverability that exceeds 7g need more particles to be tracked successfully. On the other hand, more particles mean more complexities in the tracking algorithm. In this paper, high-performance targets are tracked by fuzzy logic particle filter (FLPF); which uses fuzzy logic systems (FLS), to track targets when they start to maneuver. It estimates the angular turn rate, which is included as a state component, and tunes dynamically the number of particles used to estimate the posterior distribution. Meanwhile, when the target is moving according to a linear model, the unscented Kalman filter is used to track it. A tracker fusion technique is proposed to reduce the computation load when the target is non-maneuvering by using the unscented Kalman filter (UKF) as it has less computational load compared to the particle filters. The UKF is known to be optimal and is employed for state estimation for linear and Gaussian systems. The proposed technique performed well when tracking a high-performance target up to a maneuver equals to 13g using the model of a T-38 Talon training aircraft. Moreover, as for the computation load, it has been decreased due to the use of UKF when the target is moving according to a linear model.