A measurement set partitioning for extended target tracking using a gaussian mixture extended-target gaussian mixture probability hypothesis density filter

A new measurement set partitioning based on grid density and spectral clustering is proposed to overcome the problem that it is impossible to implement all the possible partitioning of a measurement set by the filters with extended-target Gaussian mixture probability hypothesis density. Firstly, the dynamic grid generation technique is used to acquire the grid density of measurement set, then the double-density threshold is adopted to remove the clutters of measurements set. Lastly, the spectral clustering based on the sensitive distance is applied in partitioning the measurement set from which the clutters have been removed. Simulation results show that, compared with the typical partition algorithm of measurement set, though the tracking performance of the proposed algorithm loses 5%, the computational efficiency is increased by 38%. ©, 5015, Xi'an Jiaotong University. All right reserved.