In order to track maneuvering target with Gaussian mixture probability hypothesis density（ GMPHD）filtering algorithm

a new algorithm combines improved best fitting Gaussian（ BFG） with GMPHD

that is BFGGMPHD algorithm

is proposed. The approximation method is improved in the proposed algorithm which approximates the state transition matrix and process noise covariance matrix of target kinematic model by matching the transition mean and covariance matrix. The model probability update process is introduced into the recursion of BFG to solve the problem that the recursion of BFG is only determined by priori information. Simulation experiments show that the improved BFG-GMPHD algorithm can filter out the clutter in sensor data

effectively match change of target moving model

accurately estimate number and state of targets and improve the performance of tracking.