3D AOA Target Tracking with Two-step Instrumental-variable Kalman Filtering

A two-step pseudolinear Kalman filter (2S-PLKF) was previously proposed for angle-of-arrival (AOA) target tracking in three-dimensional space drawing on the pseudolinear rearrangement of nonlinear azimuth and elevation angle measurement equations. Despite enjoying low computational complexity, this algorithm suffers from a severe bias problem arising from the nonlinear-to-linear transformation of the angle measurement equations. This paper presents a new two-step instrumental-variable Kalman filter (2S-IVKF) exploiting the use of pseudolinear estimation, as well as instrumental-variable estimation to overcome the bias problem. The performance advantage of the proposed 2S-IVKF algorithm over the 2S-PLKF, as well as extended and sigma-point Kalman filters is demonstrated with simulation examples.