Adaptively target tracking method based on double-Kalman filter in existence of outliers

In the actual course of target tracking, the measurement noise is changing at any time because of the impact of external environment, the problem of the sensors system and other reasons. Meanwhile, there are outliers in the measurements because of the clutter environment. However, the measurement noise covariance is changeless in standard Kalman filter, and the impact of outliers on the tracking results can not be eliminated by Kalman filter, so the tracking results are not perfect inevitably. This article presents a new target tracking method based on double-Kalman filter in existence of outliers, which can solve above problems by adaptively adjusting the measurement noise covariance based on the two results of Kalman filter with the different steps. The presented method can also detect the outliers in accord with the property of the innovation and reconstruct the state estimates to keep the orthogonal property of innovation and reduce the influence of outliers. The simulation results prove that the method can restrain the outliers and increase the accuracy of the target tracking system.