Fuzzy-based Error Correction Mechanism to Improve the Precision of Intelligent Maneuvering Target Tracking
Tsung-Ying Sun, Shang-Jeng Tsai, Hung-chun Chen, Shan-ming Yang · 2006
This paper proposes a fuzzy-based error correction mechanism (FECM) to improve the precision of an online data-driven fuzzy clustering (ODDFC) used in the maneuvering target tracking and trajectory prediction. In the ODDFC, the observed data are extracted automatically by fuzzy inference mechanism without much computation and training costs. But the improvement performance of ODDFC is slightly due to its parameters limitation and the prediction accuracy can be affected by the trajectory's curvature of moving target. So we propose ODDFC with FECM to solve the problem. In the proposed method, we use fuzzy inference system that has error correction mechanism to reduce the prediction error of ODDFC. ODDFC with FECM can predict maneuvering targets adapt quickly and have better prediction performance than ODDFC. Simulation results show that proposed method can improve the performance of ODDFC