Abstract
This letter presents a new three-dimensional (3-D) instrumental variable based Kalman filter (3D-IVKF) algorithm for angle-of-arrival target tracking from azimuth and elevation angle measurements. First, a 3-D pseudolinear Kalman filter (KF) algorithm is derived by applying the classical linear KF to a pseudolinear state-space model. To counter the severe bias problems with this algorithm, bias compensation and recursive instrumental variable (IV) methods are considered. A selective-angle-measurement strategy is also adopted to satisfy requisite conditions for IV estimation. The resulting 3D-IVKF algorithm inherits the low computational complexity and robust performance of pseudolinear estimation techniques. It is shown through simulation examples that the 3D-IVKF algorithm can achieve a similar tracking performance to the sigma-point KFs by producing a negligible bias and a mean-square error close to the posterior Cramer–Rao lower bound at a much reduced computational complexity, while outperforming the extended KFs at high noise levels.
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