Multi-observation Stations Fitness-Weighted Target Tracking Algorithm based on UKF-PF Hybrid Filtering

To address such shortcomings as low measurement accuracy, small range and poor reliability of single observation station and single filtering algorithm in target tracking, a multi-observation stations fitness-weighted target tracking algorithm based on unscented Kalman filter (UKF)-particle filter (PF) hybrid filtering is proposed in this paper. Specifically, the equal weight fusion method is firstly employed to construct a multi-observation stations target tracking model, and a multi-observation stations target tracking algorithm based on UKF and PF is designed. Next, the algorithm is improved by designing the fitness function and the weight calculation method of the observation station to assign different weights to each observation station. Finally, a multi-observation stations fitness-weighted target tracking algorithm based on UKF-PF hybrid filtering is devised. Simulation results revealed that the proposed target tracking algorithm can improve the tracking effect and enhances the tracking accuracy.