  In multi-target tracking (MTT), non-Gaussian measurement noise from sensors
can diminish the performance of the Gaussian-assumed Gaussian mixture
probability hypothesis density (GM-PHD) filter. In this paper, an approach that
transforms the MTT problem under non-Gaussian conditions into an MTT problem
under Gaussian conditions is developed. Specifically, measurement noise with a
non-Gaussian distribution is modeled as a weighted sum of different Gaussian
distributions. Subsequently, the GM-PHD filter is applied to compute the
multi-target states under these distinct Gaussian distributions. Finally, an
interactive multi-model framework is employed to fuse the diverse multi-target
state information into a unified synthesis. The effectiveness of the proposed
approach is validated through the simulation results.
