To resolve the problem for multi-target tracking in the presence of association uncertainty

detection uncertainty and clutter

we derive and present a novel multi-target Bayesian filter.Instead of maintaining the joint posterior density of the multi-target state

the proposed Bayesian filter jointly propagates the marginal distribution for each target and their existence probabilities.We also develop an approximation implementation algorithm of the marginal distribution Bayesian (MDB) filter for a nonlinear Gaussian system where the unscented transform technique is employed to deal with the nonlinearities of target dynamic and measurement models.The simulation results demonstrate that the proposed filter achieves better tracking performance of multiple targets than the probability hypothesis density (PHD) filter.