3D Angle of Arrival Target Tracking with Unbiased Pseudo-Linear Kalman Filter

Abstract

In the research on 3D angle of arrival target tracking, pseudo-linear Kalman filter(PLKF) has received great attention due to its low computational complexity and insensitivity to initial errors. However, the correlation between the observation matrix and the noise will cause a certain deviation in the target state’s estimation of PLKF. In view of this problem and the actual situation that the observation station has positioning errors, a 3D-modified unbiased pseudo-linear Kalman filter(3D-MUBKF) algorithm was proposed in this paper. Firstly, the overall pseudo-linearization of the azimuth and elevation observation equations was carried out, and the influence of the observation station positioning errors on the tracking accuracy was reduced by modifying the noise covariance matrix. Secondly, by separating the noise in the observation matrix, the estimation bias caused by the correlation between the observation matrix and the observation noise was reduced. The simulation analysis results show that the proposed algorithm effectively improves the accuracy of 3D angle of arrival target tracking in both non-maneuvering and maneuvering scenarios and has low computational complexity.

Keywords

• target tracking
• angle of arrival
• kalman filter
• pseudo-linear estimation
• positioning error