论文信息 - Nonlinear Target Identification and Tracking Using UKF

Nonlinear Target Identification and Tracking Using UKF

In this paper, we implemented target identification algorithm using Dempster Shafer theory (DST) and tracking algorithm using extended Kalman filter (EKF) and unscented Kalman filter (UKF). A tracking filter is developed and simulated based on UKF to track nonlinear, ballistic and reentry targets. Comparison of EKF and UKF for nonlinear targets tracking are presented based on the simulation results. Simulated results gives more supporting points to use UKF for nonlinear target tracking rather using EKF.

Uday B. Desai | S. N. Merchant | Dilip Gopichand Khairnar

[1] S. Haykin. Kalman Filtering and Neural Networks , 2001 .

[2] A. W. Rihaczek,et al. Theory and Practice of Radar Target Identification , 2000 .

[3] V. G. Nebabin. Methods and techniques of radar recognition , 1994 .

[4] Jeffrey K. Uhlmann,et al. New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[5] C.R. Smith,et al. Radar target identification , 1993, IEEE Antennas and Propagation Magazine.

[6] Engin Avci,et al. Intelligent target recognition based on wavelet packet neural network , 2005, Expert Syst. Appl..

[7] Thierry Denoeux,et al. A k-nearest neighbor classification rule based on Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[8] Jeffrey K. Uhlmann,et al. Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.