A new Gaussian mixture particle CPHD filter for multitarget tracking

When the number of targets is unknown or varies with time, multitarget state and measurements are represented as random sets and the multitarget tracking problem is addressed by calculating the first moment of the joint distribution, the probability hypothesis density (PHD), recursively. The PHD filter has been generalized to the cardinalized PHD (CPHD) filter, which propagates not only the PHD but also the full probability distribution on target number. In this paper, a new particle CPHD filter in the Gaussian mixture framework is presented. The CPHD filter uses a bank of Gaussian particle filters (GPFs) to approximate each Gaussian component and does not require clustering to determine target states. Moreover, Quasi-Monte Carlo (QMC) integration method is introduced to approximating the prediction and update distributions of target states. A challenging passive bearings-only multitarget tracking scenario is used to evaluate the performance of the proposed CPHD filter.