A recently established method for multi-target tracking which both estimates the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms is the probability hypothesis density (PHD) recursion. The approach involves modeling the respective collections of targets and measurements as random finite sets and to propagate the posterior intensity, which is a first order statistic of the random finite set of targets, in time. A closed form solution to the PHD filter recursion for multi-target tracking is provided by the Gaussian Mixture Probability Hypothesis Density filter (GM-PHD filter), whose posterior intensity function is estimated by a sum of weighted Gaussian components, including means, weights and covariances that can be propagated analytically in time. Besides the GM-PHD filter algorithm implementation, choose the probability density function for representing target births in GM-PHD recursion and true target trajectory generation to get best tracking performance is a challenge and is the purpose of this thesis work. One reference to judge the performance of the algorithm is the target detection time, as given in this thesis.