Abstract
Two new algorithms are presented in this paper to solve the bearing only tracking problem, by using the recently proposed algorithms for nonlinear filtering, which are the Unscented Kalman Filter (UKF) and the Cubature Kalman Filter (CKF). These filters are used to solve the high intrinsic nonlinearity in the modified polar coordinates dynamic model. The resulting filters, named the Modified Polar Unscented Kalman Filter (MPUKF) and the Modified Polar Cubature Kalman Filter (MPCKF) are applied to the two dimensional bearing-only tracking problem. The estimation performance of these nonlinear filters is assessed by means of the root mean square error (RMSE). The simulation results show that these new filters are more efficient than other similar filters, such as the Modified Polar Extended Kalman Filter (MPEKF). Compared to this filter, the MPUKF and the MPCKF are, in particular, non-sensitive to the initial range and more stable, by using a dedicated initialization procedure, proposed in this paper.
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