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

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.