Bayesian filter is an efficient approach for multi-target tracking in the presence of clutter. Recently, considerable\nattention has been focused on probability hypothesis density (PHD) filter, which is an intensity approximation of\nthe multi-target Bayesian filter. However, PHD filter is inapplicable to cases in which target detection probability\nis low. The use of this filter may result in a delay in data processing because it handles received measurements\nperiodically, once every sampling period. To track multiple targets in the case of low detection probability and to\nhandle received measurements in real time, we propose a sequential measurement-driven Bayesian filter. The\nproposed filter jointly propagates the marginal distributions and existence probabilities of each target in the filter\nrecursion. We also present an implementation of the proposed filter for linear Gaussian models. Simulation results\ndemonstrate that the proposed filter can more accurately track multiple targets than the Gaussian mixture PHD\nfilter or cardinalized PHD filter.
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