Multisensor multitarget tracking based on a matrix reformulation of the GM-PHD filter

The Probability Hypothesis Density (PHD) filter is a more tractable alternative to the Random Finite Set (RFS) based optimal multitarget Bayes recursion. In this paper, a matrix reformulation of the Gaussian Mixture PHD (GM-PHD) filter is introduced. Thus a new multisensor GM-PHD filter is constructed based on the matrix reformulation. Simulation results show it can be used in some applications when the sequential GM-PHD filter fails, and outperforms the sequential GM-PHD filter when those sensors have poor detection probabilities.