Simulation analysis of EKF and UKF implementations in PHD filter
Xiaoying Wang, Jiacun Wang · 2016
The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on finite set statistics. This paper presents two extensions implementation to nonlinear models in PHD filters, namely the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), and discusses their advantage and disadvantage. The simulation scenarios with different target numbers are presented while OSPA distance, clutter parameters and execute time comparisons are also analyzed as a guide for further application research.