An improvement to the linear jump Markov system Gaussian mixture probability hypothesis density filter for maneuvering target tracking
Zhang Shicang, Jianxun Li, Wu Liangbin · 2012
An improvement approach to the linear Gaussian jump Markov system (LGJMS) Gaussian Mixture probability hypothesis density (GM-PHD) filter is designed for multiple maneuvering targets tracking. This method, which called mixture LGJMS GM-PHD (MLGJMS GM-PHD) filter, is based on the theory of generalized psuedo Bayes of the first order after the update step of LGJMS GM-PHD. Compared with the existing LGJMS GM-PHD filter, simulation results show that the designed filter weights over the original one.