Mahler’s Probability Hypothesis Density (PHD filter), proposed in 2000, addresses the challenges of the multipletarget
detection and tracking problem by propagating a mean density of the targets in any region of the state
space. However, when retrieving some local evidence on the target presence becomes a critical component of
a larger process - e.g. for sensor management purposes - the local target number is insufficient unless some
confidence on the estimation of the number of targets can be provided as well. In this paper, we propose a
first implementation of a PHD filter that also includes an estimation of localised variance in the target number
following each update step; we then illustrate the advantage of the PHD filter + variance on simulated data from
a multiple-target scenario.