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The standard formulation of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters assumes that the target birth intensity is known a priori. In situations where the targets can appear anywhere in the surveillance volume this is clearly inefficient, since the target birth intensity needs to cover the entire state space. This paper presents a new extension of the PHD and CPHD...
The paper makes two contributions. First, a new formulation of the PHD filter which distinguishes between persistent and newborn objects is presented. This formulation results in an efficient sequential Monte Carlo (SMC) implementation of the PHD filter, where the placement of newborn object particles is determined by the measurements. The second contribution is a novel method for the state and error...
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