Tracking Algorithm Based on Moving Slide Window for Manuevering Target

Title & Authors
Tracking Algorithm Based on Moving Slide Window for Manuevering Target
Bae, Jinho; Lee, Chong Hyun; Jeon, Hyoung-Goo;

Abstract
In this paper, we propose a novel tracking algorithm called slide window tracker (SWT) suitable for maneuvering target. To efficiently estimate trajectory of moving target, we adopt a sliding piecewise linear window which includes past trace information. By adjusting the window parameters, the proposed algorithm is to reduce measurement noise and to track fast maneuvering target with little computational increment as compared to $\small{{\alpha}-{\beta}}$ tracker. Throughout the computer simulations, we verify outstanding tracking performance of the SWT algorithm in noisy linear and nonlinear trajectories. Also, we show that the SWT algorithm is not sensitive to initial model parameter selection, which gives large degree of freedom in applying the SWT algorithm to unknown time-varying measurement environments.
Keywords
Schur algorithm;Slide window tracker;$\small{{\alpha}-{\beta}}$ tracker;Maneuvering target;
Language
English
Cited by
References
1.
J. Yoo, J. Bae, J. Kim, J. Chun, and J. Lew, "PC-based implement of the maritime radar display unit," Conference Record of the Thirtieth Asilomar Conference, Monterey, vol. 1, pp. 474-480 1996.

2.
J. Han, M. S. Andrews, J. Bae, J. Lee, and H. G. Jeon, "Maritime Radar Simulator based on DSP Board using Switched Slide Window Tracker," Oceans'08 MTS/IEEE Quebec, vol. 1, pp. 1-4, 2008.

3.
R. E. Kalman and R. S. Bucy, "New results in linear filtering and prediction Theory," J. Basic Eng., ASME Trans. ser. D, vol. 83, pp. 95-107, 1960.

4.
T. Kawase, H. Tsurunosono, N. Ehara, and I. Sasase, "An adaptivegain alpha-beta tracker combined with circular prediction for maneuvering target tracking," IEEE TENCON, Brisbane, vol. 1, pp. 795-798, 1997.

5.
K. C. Chan, V. Lee, and H. Leung, "Radar tracking for air surveillance in a stressful environment using a fuzzy-gain filter," IEEE Trans. Fuzzy Syst, vol. 5, no. 1, pp. 80-89, 1997.

6.
A. Papoulis, Probability, random variables, and stochastic process, McGRAW-HILL, 1991.

7.
H. G. Jeon and E. Serpedin, "A novel simplified channel tracking method for MIMO-OFDM systems with null sub-carriers," Signal Processing, vol. 88, pp. 80-89, 2008.

8.
F. R. Bach, G. R. G. Lanckriet, M. I. Jordan, "Multiple kernel learning, conic duality, and the SMO algorithm," Proc. of the 21th International Conference on Machine Learning, 2004.

9.
Jaeil Lee, Ju-Hyung Lee, Jong-Wu Hyun, Chong Hyun Lee, Jinho Bae, Dong-Guk Paeng, Jungsam Cho, Taein Kang, and Nobok Lee, "Surveillance-Alert System based on USN using PDR sensors," Journal of The Institute of Electronics and Information Engineers, vol. 48, no. 12, pp. 54-61, 2011.

10.
Jeonghee Han, Chong Hyun Lee, Dong-Guk Paeng, Jinho Bae, and Won-Ho Kim, "Parametric Array Sonar System Based on Maximum Likelihood Detection," Journal of The Institute of Electronics and Information Engineers, vol. 48, no. 1, pp. 25-31, 2011.