Abstract:

The measurement-driven adaptive target birth intensity cardinalized probability hypothesis density (ATBI-CPHD) filter can track multiple targets without prior information of target birth intensity. The association between measurements and estimates is almost not considered in the intensity update stage, however, it is obviously inefficient. An efficiency-enhanced ATBI-CPHD filter based on the association is presented to correct it. Firstly, the ATBI-CPHD filter is implied in the Gaussian mixture framework. Secondly, a novel measurement-estimate association method is presented, and the search scheme using the measurement gate is designed to find out the most likely newborn measurements from those disassociated measurements and create the newborn targets. And a track management method based on the Gaussian term label and the measurement label is proposed and applied to the ATBI-CPHD filter. The simulation results prove that, compared with the traditional methods, the proposed approach leads to better efficiency and estimate accuracy.