A new heuristic for multisensor PHD filter

A new approximation to carry out the measurement update step for the multisensor PHD filter is introduced. This method approximates Bayes posterior process as the union of detected and missed targets. The detected target pdfs are extracted around approximately maximum likelihood estimate of target states. Reduced Palm intensity function averaged among sensors is used to approximate the intensity due to the missed targets. For low probability of detection and/or higher false alarm rates, compared to the iterated corrector heuristic the new approximation is shown to provide sharper peaks around target states.