Tracking for maneuvering target trajectories via the 3D circular filter

A circular prediction algorithm is proposed, which integrates the measured data into the filter and constrains the prediction to lie on a smooth curve modeled by an arc of a circle. The circular prediction is entirely defined in relation to three measurements in three-dimensional space. It is therefore not necessary to calculate the center and the radius of the circle. To obtain the statistics of the circular prediction, the unscented transformation has been utilized. The proposed hybrid filter combines the circular prediction and a constant velocity prediction by utilizing the covariance intersection (CI). This combined prediction can be updated with the subsequent measurement using a linear estimator. The proposed technique is compared with standard filters and the interacting multiple model (IMM) approach on a benchmark trajectory which includes coordinated turns and straight line maneuvers.