Pseudo steady state filters for target tracking with polar measurements

Target tracking under the linear and Gaussian assumption is a well known problem. In this case, the optimal state estimate is delivered by the standard Kalman filter. However, for real world systems, this assumption is not satisfied. A commonly encountered problem for the tracking community is: target tracking in a Cartesian frame of reference, with measurements delivered by radar in polar coordinates. In this paper we propose two novel static versions of the Debiased Converted Measurements Kalman Filter (DCMKF) and the Unscented Kalman Filter (UKF), that use the well known αβ filter. The proposed filters are: the Debiased Converted Measurement αβ Filter (DCMαβ) and the Unscented αβ filter (Uαβ). The RMSE calculated by means of Monte Carlo simulations is used to assess the performances of these new filters. The simulations results show that, the proposed filters are much simpler and thus 60% less computational time demanding and have acceptable RMSE compared to that of the classical ones. These filters are also suitable for parallel implementation, which makes them potential candidates for real time applications on embedded systems.