Abstract:

The standard cardinalized probability hypothesis density (CPHD) filter is a promising algorithm for multi-target tracking. However,due to its assumption that the target birth intensity is known a priori，it cannot work well in the situations where targets can appear anywhere in the surveillance region. To solve this problem,a one step initializing Gaussian mixture CPHD (GMCPHD) filter is proposed to adaptively initialize the newborn targets using the measurements far away from the current estimated multitarget states. Furthermore,Doppler information (DI) is used to initialize the velocities of the newborn targets,and in the update step position and Doppler measurements are incorporated in a serial process. Simulations show that the proposed algorithm can effectively initialize the newborn targets and improve the accuracy of target number estimation as well as the optimal subpattern assignment distance when compared with the existing algorithm.