THE PERFORMANCE ANALYSIS OF EXTENDED KALMAN FILTER ON RADAR TARGET TRACKING

Öz

THE PERFORMANCE ANALYSIS OF EXTENDED KALMAN FILTER ON RADAR TARGET TRACKING

Anahtar Kelimeler

• Radar, Target tracking, Extended Kalman filter (EKF)

Kaynakça

1. Mahafza, B., R., Radar Systems Analysis and Design Using, Chapman & Hall/CRC, United States of America, p.p.529, 2000.
2. Ahern, J., Delisle, G. Y., etc., Radar, Lab- Volt Ltd., vol. 1, p.p. 4-7 Canada, 1989.
3. Lana, “A., Multi-Target Tracking Algorithms Employing Both Kalman Filtering and Probabilistic Data Association”, M.Sc. Thesis, Graduate School of Natural and Applied Sciences, p.p.77, 2001. Technical University,
4. Gulec,U., Maneuvering Target Tracking, M.Sc. Thesis Gazi University Institute of Science and Technology, p.p.114, 2000.
5. Blackman, S. S., Multi-target Tracking with Radar Application, Artech House, 1986.
6. Dungate, D., Theobald, R., Nurse, F., “Higher-order Kalman filter to support fast target tracking in a multi-function radar system”, Target Applications (Ref. No. 1999/090, 1999/215), IEE
7. Colloquium on , 11-12 Nov. Page(s): 14/1 -14/3, Algorithms and Que, W., Peng, Y., Lu, D., Hou, X., “A new approach to data fusion for stealth targets tracking”, Radar 97 (Conf. Publ. No. 449) , 14- 6 Oct. 1997 Page(s): 657 -661, 1997.
8. Salmond, D., “Target tracking: introduction and Kalman tracking filters”, Target Tracking: Algorithms and Applications (Ref. No. /174), IEE , Volume: Workshop , Page(s): 1/1 -1/16 vol.2, 2001.

9. Turkoglu, I., Arslan A., Feature Extractıon Method Based On Power Spectrum of Ar Model And Artificial Neural Network For Target Classification In The Pulse Radars”, Gazi University, Technical Education Faculty, Journal of Polytechnic , 5(2) , p.p. 121-127, (2002).
10. Sun, H. Ve Chiang, S., “Tracking Multitarget in Cluttered Envirenment”, IEEE Trans. on Aerospace and Elec. Systems Vol. 28 No. 2, p.p.546-559, 1992.
11. Wilmut, M.J., Ozard, J.M., Woods, B., An efficient target tracking algorithm for matched field processing”, OCEANS '93. 'Engineering in Harmony with Ocean'. Proceedings , 18-21
12. Oct. 1993 Page(s): III/81 -III/85 vol.3, 1993.
13. Chen, B., and friends, “Tracking of Multiple Maneuvering Targets in Clutter Using IMM/JPDA Filtering and Fixed-lag Smoothing”, Automatica 37, p.p. 239-249, 2001.
14. Wang, T. C. and Varshney, P. K., “A Tracking Targets”, IEEE Trans. on Aero Elec. Sys., Vol. No.3, p.p.910-924, 1993. Maneuvering and Dayan, J., “Interacting Multiple Model
15. Methods in Target Tracking: a survey”, IEEE Trans. on Aero Elec. Systems, Vol. 34, No.1, p.p. 103-119, 1998.
16. Mehrotra, K. Ve Mahapatra, P. R., “A jerk Model for Tracking Highly Manueuvering Targets”, IEEE Trans. on Aerospace and Elec. Systems Vol.33 No.4, p.p.1094-1105, 1997.
17. Tracking”, IEEE Trans. On Aero Elec Sys 31: (1), p.p. 458-468, 1995. 1 Time (s)
18. Fig. 1. Position estimate error of the EKF 200 Time (s) Fig. 2. The real target position and estimated target position by the EKF Time (s) Fig. 3. Target velocity estimate error of the EKF Time (s) Fig. 4. The real target velocity and estimated target velocity by the EKF Time (s) Fig. 5. Target acceleration knowledge estimate error of the EKF Time (s) Fig. 6. The real target acceleration knowledge and estimated target acceleration knowledge by the EKF