Abstract
The conventional target tracking approaches are presented under the assumption of the point target. In multi extended target scenarios, the tracking performance of these approaches may be greatly decreased. The the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) (named as the ET-GM-PHD approach) has been presented for applying the GM-PHD approach into extended target tracking. However, it has been proposed under the linear models. In fact, most of targets are moving with nonlinear models. Thus, we, in this paper, present a square-root cubature information filter (SCIF) based ET-GM-PHD approach. To be more specific, we, first, employ the cubature points to predict the mean and the square-root factor of covariance. Then, the information forms of the mean and square-root of covariance has been used to update the mean and covariance of GM component. Meanwhile, we integrate the gating method into our method for saving computational complexity. Owing to the significant tracking performance of the SCIF method, our approach can estimate states and number of multi extended targets in nonlinear scenarios. In addition, we also propose an observation driven method to initiate the birth intensity. As for our method, the conditional probability has been adopted to describe the association between the target and its corresponding observations. With such a probability, the most possible partition, where the estimated targets belong to, can be approximated. Thus, the birth intensity can be estimated by removing the cells associated with the estimated targets. Since we use the estimated targets to initiate the birth intensity, our approach can initiate the birth intensity adaptively. The simulation results prove the effectiveness of our approach.
A Gaussian mixture PHD filter for extended target tracking
