Abstract: In the traditional GM-PHD filter,all measurements received at current time are used to update different types of targets.Much time is spent on updating targets because of using invalid measurements.A kind of fast multi-target tracking filter was proposed in this paper.Firstly,Gaussian components are divided into two parts.One part is birth targets and the other is survival targets.Then the residuals between survival targets and all measurements are calcula-ted.Next,only the measurements which fall in the elliptical gate are used to update survival targets.Similarly,the resi-duals between birth targets and remaining measurements are calculated,and only those measurements which fall in the elliptical gate are used to update birth targets.In this way,we could minimize invalid measurements and reduce the computing complexity.The experimental results show that the new method not only reduces the time complexity greatly,but also insures the accuracy of target tracking.Its performance is better than the traditional GM-PHD filter as a whole.

Key words: Multi-target tracking,Gaussian mixture probability hypothesis density filter,Elliptical gating,Measurements partition