Achieving Adaptive Tracking Through Adjusting of Process Noise
by
Karim A. Fouad
School of Astronautics, Beijing University of Aeronautics and Astronautics, BUAA
Coauthors: Dr. Xu XiaoJian

An adaptive α-β tracking filter is proposed for tracking maneuvering targets. The tracking filter gain is updated with respect to the adjusted process noise variance which is obtained via time averaging of the process over a sliding window. The tracking index for each epoch is computed by using the adjusted process noise variance. It is shown via simulation that the proposed approach provides robust and accurate position estimates during the target maneuver while the performances of the conventional Kalman filter and the conventional α-β tracker are shown much degraded. The comparison of applying the adjusting noise covariance principle to extended kalman filter (EKF), α-β filter, and unscented kalman filter (UKF) is introduced, showing the effect of applying adjusting process noise and its role to achieve adaptive tracking of maneuvering targets. The performance of the proposed principle for α-β tracker, extended Kaman filter (EKF), and unscented kalman filter (UKF) is compared with respect to the conventional candidates’ conventional filters in terms of the RMSE. The gain for each filter type is calculated on-line corresponding to the adjusted process noise that was proposed for a maneuvering target tracking in the two dimensional Cartesian coordinate. It will be shown via simulation that the proposed method follows the target successfully during maneuvers and provides more accurate and robust performance compared to the conventional Kalman filter (EKF, UKF) and the conventional α-β tracker.

Date received: April 28, 2008

Copyright © 2008 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # caxc-99.