Ground maneuvering target tracking based on the strong tracking and the cubature Kalman filter algorithms

16 March 2016 Ground maneuvering target tracking based on the strong tracking and the cubature Kalman filter algorithms

Min Gao, Heng Zhang, Yulong Zhou, Baoquan Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 2, 023006 (March 2016). https://doi.org/10.1117/1.JEI.25.2.023006

Abstract

In an effort to improve the tracking accuracy of the ground maneuvering target in infrared images, a method is proposed based on the strong tracking filter (STF) and the cubature Kalman filter (CKF) algorithms. In this method, the fading factor is introduced from the STF algorithm and is calculated by transforming the nonlinear measurement variance matrix to be linear approximately, and then the fading factor is used to correct the prediction error covariance matrix (PECM) of CKF, so that the gain matrix can be adjusted at real time and hence the tracking ability of the maneuvering target could be improved. After the digital simulation experiment, it is shown that, comparing with CKF and the unscented Kalman filter algorithms, the average tracking accuracy of the location is increased by more than 20% with the target velocity under 20 m/s and acceleration under 5 m/s², and it can even be increased by 50% when the target step maneuver occurs. With the tracking experiment on the real infrared tank images, it can be concluded that the target could be tracked stably by the proposed method, and the maximum tracking error is not more than 8 pixels even though the 180 deg turning takes place.

Citation Download Citation

Min Gao, Heng Zhang, Yulong Zhou, and Baoquan Zhang "Ground maneuvering target tracking based on the strong tracking and the cubature Kalman filter algorithms," Journal of Electronic Imaging 25(2), 023006 (16 March 2016). https://doi.org/10.1117/1.JEI.25.2.023006

Published: 16 March 2016

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE