High-Performance Target Tracking Using Tracker Fusion in a Track-While-Scan Radar

Abstract

In this paper, high-performance targets are tracked by fuzzy logic particle filter (FLPF) that uses fuzzy logic systems (FLS). 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. 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. Moreover, the computation load was decreased due to the use of UKF when the target is moving in a straight-line motion.