The Dual-Kalman Filtering and Neural Solutions to Maneuvering Estimation Problems *

Tracking maneuvering targets in a radar system is more complicated because the target accelerations cannot be directly measured. It may occur severe tracking error even diverge the estimates when the maneuvering situations are happened. In this paper, we develop a Dual-Kalman filtering algorithm to handle the maneuvering targets ’ tracking problems. In this approach, two collaborative Kalman filters are devised which one for pursuing the track estimation and the other for estimating the status of maneuver. Based on this approach, the most approximate target’s acceleration can be detected and estimated in real time. Moreover, it is also shown that one Competitive Hopfield Neural Network-based data association combined with a multiple-target tracking system demonstrates target tracking capability

The Dual-Kalman Filtering and Neural Solutions to Maneuvering Estimation Problems

[[abstract]]Tracking maneuvering targets in a radar system is more complicated because the target accelerations cannot be directly measured. It may occur severe tracking error even diverge the estimates when the maneuvering situations are happened. In this paper, we develop a Dual-Kalman filtering algorithm to handle the maneuvering targets’ tracking problems. In this approach, two collaborative Kalman filters are devised which one for pursuing the track estimation and the other for estimating the status of maneuver. Based on this approach, the most approximate target’s acceleration can be detected and estimated in real time. Moreover, it is also shown that one Competitive Hopfield Neural Network-based data association combined with a multiple-target tracking system demonstrates target tracking capability